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Pavement distress is one of the most serious and prevalent diseases in pavement road detection. However, traditional methods for crack detection often suffer from low efficiency and limited accuracy, necessitating improvements in the accuracy of existing crack detection algorithms. Consequently, we propose the shuffle attention for you only look once version eight (SA‐YOLOv8) model, which is based on an enhanced framework. Initially, we establish the required dataset and classify images proportionally based on their states. Subsequently, we conduct comparative testing against the results of the original model, analyzing issues such as the oversight of shallow and small cracks, truncation in the recognition of single‐instance long cracks, and imprecise detection. We devise an improved detection approach based on YOLOv8. This method incorporates a small target detection layer to optimize the receptive field range, aiming to focus on identifying shallow and small cracks. Simultaneously, the Shuffle Attention mechanism and the transplanted spatial pyramid pooling‐fast (SPP‐F) reuse structure are introduced in the feature extraction network to enhance the model's attention to detection targets. This augmentation improves the fusion of features for shallow small targets and overall and partial features of long cracks, thereby alleviating the precision of the model in crack detection. The experimental results demonstrate a stepwise improvement in the model's mean average precision (mAP) with each enhancement to the original network. Initially, adding a small object detection layer increased the mAP by 3.4 percentage points, raising it to 68.2%. Subsequently, incorporating the spatial attention (SA) module resulted in a more substantial improvement, boosting the mAP by 8.7 percentage points to 73.5%. Finally, the addition of the transplanted SPP‐F module further enhanced accuracy, increasing the mAP by 0.7 percentage points from the previous stage, thus achieving a final mAP of 74.2%. Overall, these modifications resulted in a total improvement of 9.4 percentage points in mAP compared to the original model. In conclusion, the proposed SA‐YOLOv8s model effectively supports the automated recognition of asphalt road surface cracks, demonstrating applicability in practical scenarios. The recognition performance is notably favorable, demonstrating robustness in complex environments. [ABSTRACT FROM AUTHOR]

Early identification of concrete cracks and multi-class detection can help to avoid future deformation or collapse in concrete structures. Available traditional detection and methodologies require enormous effort and time. To overcome such difficulties, current vision-based deep learning models can effectively detect and classify various concrete cracks. This study introduces a novel multi-stage deep learning framework for crack detection and type classification. First, the recently developed YOLOV10 model is trained to detect possible defective regions in concrete images. After that, a modified vision transformer (ViT) model is trained to classify concrete images into three main types: normal, simple cracks, and multi-branched cracks. The evaluation process includes feeding concrete test images into the trained YOLOV10 model, identifying the possible defect regions, and finally delivering the detected regions into the trained ViT model, which decides the appropriate crack type of those detected regions. Experiments are conducted using the individual ViT model and the proposed multi-stage framework. To improve the generation ability, multi-source datasets of concrete structures are used. For the classification part, a concrete crack dataset consisting of 12,000 images of three classes is utilized, while for the detection part, a dataset composed of various materials from historical buildings containing 1116 concrete images with their corresponding bounding boxes, is utilized. Results prove that the proposed multi-stage model accurately classifies crack types with 90.67% precision, 90.03% recall, and 90.34% F1-score. The results also show that the proposed model outperforms the individual classification model by 10.9%, 19.99%, and 19.2% for precision, recall, and F1-score, respectively. The proposed multi-stage YOLOV10-ViT model can be integrated into the construction systems which are based on crack materials to obtain early warning of possible future deformation in concrete structures. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach, referred to as Global Multiplexing of Original Features (GMOF), to detect cracks in images. The proposed approach leverages the shallow layers of a network to extract crack-edge details, which are then fused with deep features in later stages to improve detection accuracy. Specifically, GMOF utilises the first stage of a backbone network to extract shallow edge information, which is then resized to match the resolution of the subsequent stages. The original feature is then concatenated with the deep feature map, followed by a one-dimensional convolution to ensure channel consistency between stages. The experimental results on image classification and segmentation tasks show that GMOF helps the network learn fine edge details of cracks, resulting in a maximum improvement of 6.06% (classification) and 15.83% (segmentation) in accuracy. This crack detection method is easy to integrate into existing deep learning frameworks. [ABSTRACT FROM AUTHOR]

The current intelligent detection of asphalt pavement cracks relies on visible images, which are ineffective in handling temporary shadow and reflection. In contrast, infrared images reflect thermal radiation intensity, making them suitable for crack detection by mitigating environmental interference. The GSkYOLOv5 method is proposed in this study for accurate crack detection in infrared images of asphalt pavement. Firstly, an infrared dataset consisting of 2400 images of asphalt pavement cracks was captured and produced, including three types of cracks – strip cracks, cross cracks and reticular cracks. And the imbalanced samples were balanced using generative adversarial networks. Secondly, GhostNet and Selective Kernel Networks were added to enhance crack information perception, and the SPPF module was replaced to improve feature learning for different scale cracks. Lastly, GSkYOLOv5 was tested with a self-built infrared dataset of asphalt pavement cracks. GSkYOLOv5 improved detection accuracy by 4.7% and recall rate by 1.3% compared to YOLOv5s, outperforming the other tested algorithms. This method can be combined with visible image-based crack detection for comprehensive intelligent detection of asphalt pavement cracks using dual lenses for visible and infrared images. [ABSTRACT FROM AUTHOR]

The use of Artificial Intelligence (AI) to detect defects such as concrete cracks in civil and transport infrastructure has the potential to make inspections less expensive, quicker, safer and more objective by reducing the need for on-site human labour. One deployment scenario involves using a drone to carry an embedded device and camera, with the device making localised predictions at the edge about the existence of defects using a trained convolutional neural network (CNN) for image classification. In this paper, we trained six CNNs, namely Resnet18, Resnet50, GoogLeNet, MobileNetV2, MobileNetV3-Small and MobileNetV3-Large, using transfer learning technology to classify images of concrete structures as containing a crack or not. To enhance the model's robustness, the original dataset, comprising 3000 images of concrete structures, was augmented using salt and pepper noise, as well as motion blur, separately. The results show that Resnet50 generally provides the highest validation accuracy (96% with the original dataset and a batch size of 16) and the highest validation F1-score (95% with the original dataset and a batch size of 16). The trained model was then deployed on an Nvidia Jetson Nano device for real-time inference, demonstrating its capability to accurately detect cracks in both laboratory and field settings. This study highlights the potential of using transfer learning on Edge AI devices for Structural Health Monitoring, providing a cost-effective and efficient solution for automated crack detection in concrete structures. [ABSTRACT FROM AUTHOR]

The conditions of various nuclear power plant facilities are regularly examined through manual inspections. Remote visual inspection is commonly applied and requires engineers to watch lengthy inspection footage and seek anomaly features therein. This is a labor-intensive process as anomaly features of interest usually only appear in very short segments of the original whole video. Therefore, an automated anomaly detection system is preferred to lessen the intensive labor cost in the inspection process. The detection process could also benefit from useful information that could potentially contribute to addressing reasoning traceability. With a well-prepared training data set of the anomaly feature, a convolutional neural network (CNN) can be developed to automatically detect anomaly indications in the inspection video. However, false-positive detections may occur and can be difficult to remove without seeking manual verification. To overcome this problem, we present a new automated video-level anomaly detection framework that utilizes the latency mechanism to effectively lessen false-positive occurrences, and therefore, increase detection accuracy. In this framework, a CNN-based anomaly classifier first performs initial scanning of the anomaly type of interest in every region of the sampled frames. Then our latency mechanism is applied to refine the initial scanning results by flagging up a region as an "anomaly" indication only when "anomaly" is detected by CNN in the current frame and also in a sequence of previous consecutive frames of the same region. We present a case study of crack feature detection in superheater inspection videos to illustrate the performance of the proposed framework. The results show that the latency mechanism can effectively remove the original false-positive detections seen in the initial scanning. In order to provide a primary exploration of suggesting possible formats for addressing reasoning traceability, knowledge graphs of the reasoning process in the video-level detection framework are built to provide a better understanding of why a specific section of the video is flagged as anomaly contents by the video-level detection framework. [ABSTRACT FROM AUTHOR]

The accurate detection of tunnel lining cracks and prompt identification of their primary causes are critical for maintaining tunnel availability. The advancement of deep learning, particularly in the domain of convolutional neural network (CNN) for image segmentation, has made tunnel lining crack detection more feasible. However, the CNN-based technique for tunnel lining crack detection commonly prioritizes increasing algorithmic complexity to enhance detection accuracy, posing a challenge in balancing the accuracy of detection and the efficiency of the algorithm. Motivated by the superior performance of Unet in image segmentation, this paper proposes a lightweight tunnel lining crack detection model named Mini-Unet, which refined the Unet architecture and utilized depthwise separable convolutions (DSConv) to replace some standard convolution layers. In the optimization of the proposed model parameters, applying a hybrid loss function that integrated dice loss and cross-entropy loss effectively tackled the imbalance between crack and background categories. Several models were set up to contrast with Mini-Unet and the experimental results were analyzed. Mini-Unet achieves a mean intersection over union (MIoU) of 60.76%, a mean precision of 84.18%, and a frame per second (FPS) of 5.635, respectively. Mini-Unet outperforms several mainstream models, enabling rapid detection while maintaining identified accuracy and facilitating the practical application of AI power for real-time tunnel lining crack detection. [ABSTRACT FROM AUTHOR]

AbstractThe thrust of present research focused on identifying the crack signatures in glass/epoxy laminated composite beams (LCB) through a novel crack-detection technique using buckling data and fuzzy logic. The technique is developed on MATLAB utilizing a hybrid fuzzy inference system (FIS) that combines three membership functions: Gaussian, trapezoidal, and triangular. Critical buckling loads, computed through ABAQUS finite element (FE) simulations, serve as inputs to the FIS. The FIS results show hybridization offer superior accuracy over standalone methods, closely matching the experimental results through INSTRON 8862 Universal Testing Machine (UTM) and presents an effective approach for crack detection in buckling domain. [ABSTRACT FROM AUTHOR]

Unsupervised anomaly detection, often approached as a one‐class classification problem, is a critical task in computer vision. Knowledge distillation has emerged as a promising technique for enhancing anomaly detection accuracy, especially with the advent of reverse distillation networks that employ encoder–decoder architectures. This study introduces a novel reverse knowledge distillation framework known as RDMS, which incorporates a pretrained teacher encoding module, a multi‐level feature fusion connection module, and a student decoding module consisting of three independent decoders. RDMS is designed to distill distinct features from the teacher encoder, mitigating overfitting issues associated with similar or identical teacher–student structures. The model achieves an average of 99.3%$\%$ image‐level AUROC and 98.34%$\%$ pixel‐level AUROC on the MVTec‐AD dataset and demonstrates state‐of‐the‐art performance on the more challenging BTAD dataset. The RDMS model's high accuracy in anomaly detection and localization underscores the potential of multi‐student reverse distillation to advance unsupervised anomaly detection capabilities. The source code is available at https://github.com/zihengchen777/RDMS [ABSTRACT FROM AUTHOR]

In view of the low identification accuracy of the crack detection technology of asphalt pavement under the current conditions of complex pavement, an asphalt pavement crack detection algorithm based on improved YOLOv5s was proposed by building the dataset for asphalt pavement cracks. The first step was to make the following improvements to the original YOLOv5s model according to the characteristics of the asphalt pavement crack dataset; obtain the initial anchor box matched with the characteristics of the asphalt pavement cracks by re-clustering the anchor box of the crack dataset using the K-means + + algorithm; add the convolutional block attention module (CBAM) to the Prediction part of the model to improve the model's ability to detect small cracks; take the CloU_Loss function as the model regression loss function to improve the anchor box location accuracy. The second step was to perform an ablation experiment on the improved YOLOv5s model, Which would prove that each improvement scheme could improve detection ability without conflict. The final step was to compare the improved YOLOv5s model with various classic target detection models in the dataset of this paper; the CFD dataset, the Crack500 dataset and the Crack200 dataset. The results show that the detection of the improved YOLOv5s model on each dataset was better than other target detection models. The mAP@0.5 and mAP@ [0.5:0.95] of this model on the dataset were 90.58% and 56.08%, which were much higher than other target detection models. These findings indicate that the improved YOLOv5s model had better detection under complex pavement conditions and could provide a theoretical basis for the automatic detection of asphalt pavement cracks. [ABSTRACT FROM AUTHOR]

Cracks represent one of the most common types of damage in building structures and it is crucial to detect cracks in a timely manner to maintain the safety of the buildings. In general, tiny cracks require focusing on local detail information while complex long cracks and cracks similar to the background require more global features for detection. Therefore, it is necessary for crack detection to effectively integrate local and global information. Focusing on this, a local–global feature adaptive fusion network (LGFAF-Net) is proposed. Specifically, we introduce the VMamba encoder as the global feature extraction branch to capture global long-range dependencies. To enhance the ability of the network to acquire detailed information, the residual network is added as another local feature extraction branch, forming a dual-encoding network to enhance the performance of crack detection. In addition, a multi-feature adaptive fusion (MFAF) module is proposed to integrate local and global features from different branches and facilitate representative feature learning. Furthermore, we propose a building exterior wall crack dataset (BEWC) captured by unmanned aerial vehicles (UAVs) to evaluate the performance of the proposed method used to identify wall cracks. Other widely used public crack datasets are also utilized to verify the generalization of the method. Extensive experiments performed on three crack datasets demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This paper addresses the crucial need for effective crack detection and dimensional assessment in civil infrastructure materials to ensure safety and functionality. It proposes a cost-effective crack detection and dimensional assessment solution by applying state-of-the-art deep learning on smartphone sensor imagery and positioning data. The proposed methodology integrates three-dimensional (3D) data from LiDAR sensors with Mask R-convolutional neural network (CNN) and YOLOv8 object detection networks, for automated crack detection in concrete structures, allowing for accurate measurement of crack dimensions, including length, width, and area. The study finds that YOLOv8 produces superior precision and recall results in crack detection compared to Mask R-CNN. Furthermore, the calculated crack-straight-length closely aligns with the ground-truth straight-length, with an average error of 1.5%. These research contributions include developing a multi-modal solution combining LiDAR observations with image masks for precise 3D crack measurements, establishing a dimensional assessment pipeline to convert segmented cracks into measurements, and comparing state-of-the-art CNN-based networks for crack detection in real-life images. [ABSTRACT FROM AUTHOR]

This paper introduces a damage diagnosis method based on the reassignment method and matching networks (MNs) to study the structural health monitoring of aerospace composite material components. This aims to facilitate the mapping of signal features to complex failure modes. We introduce a signal processing technique based on the reassignment method, employing a sliding analysis window to re‐estimate local instantaneous frequency and group delay. By utilizing the short‐time phase spectrum of the signal, we correct the nominal time and frequency coordinates of the spectrum data, aligning them more accurately with the true support region of the analyzed signal. Subsequently, this paper developed a deep matching network (DMN) damage diagnosis model based on MNs. This model utilizes a convolutional neural network (CNN) to extract damage‐related features from the signal and introduces the full context embedding (FCE) method to enhance the compatibility of sample embeddings. In this process, the embeddings of each sample in the training set should be mutually independent, while the embeddings of test samples should be regulated by the distribution of training set sample data. Ultimately, the damage category of test samples is determined based on cosine similarity. We validate our model using damage sample data collected from experiments and simulations conducted under varying components and operating conditions. Comparative assessments with five mainstream methods reveal an average accuracy exceeding 96%. This underscores the exceptional recognition accuracy and generalization performance of our proposed method in cross‐operating condition fault diagnosis experiments concerning aircraft composite material components. [ABSTRACT FROM AUTHOR]

The traditional magnetic flux leakage (MFL) detection technology is difficult to meet the requirements of steel oil and gas pipeline crack detection, a new residual magnetic flux leakage (RMFL) detection technology was proposed, which directly used the residual magnetic state of the pipe wall after MFL detection to realize the effective detection of small opening cracks. Compared with magnetic leakage signal, remanent signal had lower signal-to-noise ratio and higher requirement on sampling frequency of sensor. Therefore, a spatial resolution method was proposed to optimize sampling frequency of remanent signal. Firstly, the dynamic magnetization model of pipeline was established by finite element simulation, the crack characteristic data was extracted, and the distribution law was analyzed. Secondly, the frequency domain analysis method was used to optimize the sampling frequency of residual magnetic signal. Finally, the effectiveness of the crack remanence detection method based on spatial resolution was verified by experiments. The results show that the remanent signal based on spatial resolution is highly consistent with the original signal, and there are undistorted sampling points at the poles of the signal waveform, which preserves the key features of the original signal and provides an important data basis for quantifying the crack size. This technology can realize the effective detection of cracks in small openings of pipelines, and has strong engineering practicality and operability, which is conducive to the further development of crack detection technology in oil and gas pipelines. [ABSTRACT FROM AUTHOR]

Road cracks are a common road traffic safety problem. Methods such as manual measurement of cracks do not facilitate large-scale inspections, which affects the normal operation of roads and the safety of pedestrians and vehicles. In this paper, a visual measurement technique based on convolutional neural networks and morphological operations is proposed for automated and efficient detection of road cracks. This method adopts the U-Net network as the infrastructure, changes the backbone feature extraction network to the VGG-16 network and introduces multiple Indicators to establish a new loss function to alleviate the sample imbalance problem. Finally, the crack features are combined to perform morphological operations on the image to enrich the recovered detail features. After experiments on the road crack dataset, this method has better crack segmentation capability and reliability compared to other algorithms. [ABSTRACT FROM AUTHOR]

AbstractSelf-built masonry structures are commonly used as houses in rural areas of the central plains of China. These structures pose significant safety risks due to the low level of self-building and non-standard designs. The primary type of damage to the walls of masonry houses is cracks, while the decorative layers of the walls may also experience peeling or cracking. The shapes, sizes, and surface characteristics of these damages are complex and unique to each type. To achieve fast identification of surface damage on masonry house walls, this paper proposes a cascade detection model that combines a multilevel cascade classifier with a parallel sub-segmentation network. The VGG16 neural network is used as the backbone for a multi-level series classifier. The model is trained by using a damage image set of the wall decorative layers in rural masonry houses. The classification of background and damage, as well as the classification of cracks and peels, are sequentially completed. Then, the parallel sub-segmentation model uses EfficientNet-B7 as the encoder and combines it with the U-Net framework skeleton to perform pixel-level segmentation of peeling and cracks. Finally, the output of parallel sub-segmentation networks is superimposed and fused to generate a complete image containing segmentation information of peeling and cracks. The cascade form network structure adopted in this model significantly reduces the training difficulty and enhances the model accuracy. Compared with Segformer and Deeplabv3+ network, the average IoU of the proposed method for on-site masonry wall images can be increased by 0.29 and 0.08, respectively. [ABSTRACT FROM AUTHOR]

The combination of a rotor and bearings plays a pivotal role in various industrial applications, including gas turbines, compressors, turbochargers, aircraft engines, and wind generators. Ensuring the structural integrity of the rotor-bearing assembly during static and dynamic conditions is most important, as any failure could lead to unpredictable losses. Due to their continuous operation, high-speed rotors are more susceptible to faults such as misalignment, bow, eccentricity, and cracks. Among these faults, cracks are the most predominant fault that affects the system’s reliability. Despite significant advancements in rotor dynamics, cracks still threaten the integrity and performance of the rotor-bearing assembly. This review paper aims to thoroughly review the challenges and issues associated with crack formation in high-speed rotor systems. Various crack detection methods are also explored using vibration signals obtained from the rotor-bearing unit and their associated processing techniques. This study also gives an extensive overview of different crack modelling and analysis techniques. Furthermore, a novel rotor-bearing model is modelled using the finite element method, and the numerical results obtained from this model are validated with the existing literature model. This review paper will serve as a valuable resource for researchers that offers insights into the latest advancements and emphasises the importance of crack detection for ensuring the reliability of high-speed rotor systems. [ABSTRACT FROM AUTHOR]

During the industrial pecan shelling process, kernels are often damaged. To address this problem, a study is conducted to experimentally determine improved impactor geometries for end-to-end pecan cracking. Four impactors of varying internal angles (from 30° to 52.5°, in increments of 7.5°) are tested. After cracking, the pecans are passed through an image analysis software designed to detect and measure cracks in their shells. These measurements help classify each pecan into one of four categories: under crack, standard crack, ideal crack, or over crack. Cracked and ideally cracked pecans are preferred for their processability, so the impactor geometries are then evaluated based on their ability to maximize these crack types across the widest impact energy range. For the four impactors tested, the 30° impactor is found to more consistently produce preferred cracks in a larger energy range relative to the other impactors. [ABSTRACT FROM AUTHOR]

Crack is one of the main factors threatening the safety of the dam. Automatic image object detection is the main way of underwater dam crack detection. However, the traditional methods have problems with low crack detection speed, high false alarm rate, and poor robustness. In addition, the existing methods cannot get a satsifying detection result with a high detection speed. To solve these problems, we propose an efficient dam crack detection method for underwater scenes, called CrackYOLO. Firstly, to better integrate the multi-scale features without incurring excessive computational costs, we propose a feature fusion module in CrackYOLO. Next, we re-design the skip-connection in the network to get better features, compressing the overall model parameters. Then, we propose a feature extraction module called Res2C3, which combines semantic and location information. After that, we proposed a BCAtt to make features focus on both channel and location information. Finally, according to the characteristics of dam underwater crack images, we use a genetic algorithm to select the best value of hyperparameters of the model. The experimental results show that the proposed method detects underwater dam cracks robustly with less computational cost. Our CrackYOLO can get 94.3% mAP[0.5] and 151 FPS in underwater crack detection task which can achieve a real-time detection in practice. [ABSTRACT FROM AUTHOR]

In recent years, detection methods based on deep learning have received widespread attention in the field of concrete crack detection. In view of the shortcomings of traditional image detection methods, a concrete crack detection method based on feature fusion is proposed. The Fourier frequency domain processed image is used as the input of the deep learning neural network. The original time domain image and the frequency domain image are respectively input into two feature extraction modules to extract high-level features, and then the two features are fused to fully characterize the characteristics of the time domain and frequency domain, and finally the concrete crack detection results of the feature fusion are obtained. The performance of the proposed method is compared with VGG-16, AlexNet and DenseNet. Experiments show that the accuracy of the proposed method is higher than VGG-16, AlexNet and DenseNet. The proposed method has good results in concrete crack detection. To verify the generalization ability of the proposed model, the Concrete Crack Images for Classification data set was input into the proposed model for testing. The experimental results show that the proposed model has good generalization ability. [ABSTRACT FROM AUTHOR]

High-strength bolts are crucial load-bearing components of wind turbine towers. They are highly susceptible to fatigue cracks over long-term service and require timely detection. However, due to the structural complexity and hidden nature of the cracks in wind turbine tower bolts, the small size of the cracks, and their variable propagation directions, detection signals carrying crack information are often drowned out by dense thread signals. Existing non-destructive testing methods are unable to quickly and accurately characterize small cracks at the thread roots. Therefore, we propose an ultrasonic phased array element arrangement method based on the Fermat spiral array. This method can greatly increase the fill rate of the phased array with small element spacing while reducing the effects of grating and sidelobes, thereby achieving high-energy excitation and accurate imaging with the ultrasonic phased array. This has significant theoretical and engineering application value for ensuring the safe and reliable service of key wind turbine components and for promoting the technological development of the wind power industry. [ABSTRACT FROM AUTHOR]

Cracks are a common defect in civil infrastructures, and their occurrence is often closely related to structural loading conditions, material properties, design and construction, and other factors. Therefore, detecting and analyzing cracks in civil infrastructures can effectively determine the extent of damage, which is crucial for safe operation. In this paper, Web of Science (WOS) and Google Scholar were used as literature search tools and "crack", "civil infrastructure", and "computer vision" were selected as search terms. With the keyword "computer vision", 325 relevant documents were found in the study period from 2020 to 2024. A total of 325 documents were searched again and matched with the keywords, and 120 documents were selected for analysis and research. Based on the main research methods of the 120 documents, we classify them into three crack detection methods: fusion of traditional methods and deep learning, multimodal data fusion, and semantic image understanding. We examine the application characteristics of each method in crack detection and discuss its advantages, challenges, and future development trends. [ABSTRACT FROM AUTHOR]

One of the key tasks to ensure infrastructure safety is the periodic detection of bridge cracks. Since manual crack detection is subjective and inefficient, it is very important to develop an automatic crack recognition system by using machine vision. Inspired by the pyramid vision transformer (PVT) and the feature pyramid network (FPN) variants, a method combining PVT, residual transformer (REST), holistically nested edge detection (HED), and downstream detection tasks is proposed in this paper, which is named EVPTMFF (efficient visual pyramid transformer and multiple-feature fusion). Based on the PVT, the multiheaded attention module was replaced and the efficient attention module was adopted, which could process the data efficiently and flexibly. To improve the performance of EVPTMFF, the original perceptual field windows were changed. The adjacent windows were partially overlapped, which was more conducive to feature interaction and improves detection performance. To prove the generalization ability of the model, three different data sets related to bridges were collected and formed. We carried out experiments on these three data sets, and EVPTMFF showed good results. Especially for larger data sets, the performance advantage was more significant. Practical Applications: The crack detection model proposed in this paper presents a good detection effect under different illumination and interference. And the detection results, collected data, time, and other information are saved to the bridge crack detection software. This can help engineers quickly and accurately detect cracks on the bridge surface, as well as predict the development trend of cracks and possible safety issues. In practical application, the bridge crack detection system can help engineers find and solve the bridge crack problem in time and avoid the security risks and economic losses caused by cracks. At the same time, the efficiency and accuracy of bridge maintenance can be improved, and the maintenance cost and time can be reduced. The bridge crack detection system can be integrated with other hardware equipment and management systems to form a complete bridge management platform, which contributes to the traffic construction and social and economic development of the city. [ABSTRACT FROM AUTHOR]

Concrete cracking is one of the most significant damage types in reinforced concrete structures due to its potential to adversely affect durability, safety, and serviceability and even reduce the bearing capacity during operation. Thus, damage inspection of damage caused by concrete cracking is important for management, maintenance, and structural assessment for both damaged and undamaged existing buildings but with concrete cracking after a long time of use that needs reconstruction or renovation. This study provides an improved building damage inspection approach by applying Unmanned Aerial Vehicles (UAVs) and state‐of‐the‐art deep learning algorithms to detect concrete cracks on building surfaces. Two distinct architectures for Convolutional Neural Networks (CNNs), namely ResNet50 and YOLOv8 based on classification, and object detection approaches to create a total of 11 models are established, trained, and compared. The classification models attained accuracy levels exceeding 99%, whereas the object detection models achieved approximately 85%. All models effectively identified and accurately located concrete cracks on building surfaces. Besides, the CNN models' capacity to detect cracks is influenced by a variety of model hyperparameters, encompassing factors such as model architecture, the number of network layers, different training dataset sizes, and the quantity of trainable parameters necessary to learn the specific features of detection targets during the training process. The results of this study ultimately demonstrate that the proposed approach can yield accurate detection results and holds high potential for application in crack inspection to advance automatic damage inspection in building structures to a greater extent. In addition, it is important to note that a universal rule cannot be established rule as a larger and more complex model, or an increased number of trainable parameters, necessarily leads to improved detection performance. Models that are trained from scratch using local datasets might not necessarily result in enhanced performance in comparison to the improvements gained through fine‐tuning via transfer learning. Therefore, an appropriate training type, dataset size, task complexity, computational resources, and time demands to achieve a balance between accuracy and efficiency should be considered for specific application scenarios. [ABSTRACT FROM AUTHOR]

Pavement cracks are crucial indicators for assessing the structural health of asphalt roads. Existing automated crack detection models depend on large quantities of precisely annotated crack sample data. The irregular morphology of cracks makes manual annotation time-consuming and costly, thereby posing challenges to the practical application of these models. This study proposes a pavement crack image detection method integrating fractal dimension analysis and semi-supervised learning. It identifies the self-similarity characteristics within the crack regions by analyzing pavement crack images and using fractal dimensions to preliminarily determine the candidate crack regions. The Crack Similarity Learning Network (CrackSL-Net) is then employed to learn the semantic similarity of crack image regions. Semi-supervised learning facilitates automatic crack detection by combining a small amount of labeled data with a large volume of unlabeled image data. Comparative experiments are conducted on two public pavement crack datasets against the HED, U-Net, and RCF models to comprehensively evaluate the performance of the proposed method. The results indicate that, with a 50% annotation ratio, the proposed method achieves high-precision crack detection, with an intersection over union (IoU) exceeding 0.84, which is close to that of U-Net. Visual analysis of the detection results confirms the method's effectiveness in identifying cracks in complex environments. [ABSTRACT FROM AUTHOR]

Automatic crack detection is important for efficient and economical pavement maintenance. With the development of Convolutional Neural Networks (CNNs), crack detection methods have been mostly based on CNNs. In this paper, we propose a novel automatic crack detection network architecture, named hybrid dilated and global convolutional networks. Firstly, we integrate the hybrid dilated convolution module into ResNet-152 network, which can effectively aggregate global features. Then, we use the global convolution module to enhance the classification and localization ability of the extracted features. Finally, the feature fusion module is introduced to fuse multi-scale and multi-level feature maps. The proposed network can capture crack features from a global perspective and generate the corresponding feature maps. In order to demonstrate the effectiveness of our proposed method, we evaluate it on the four public crack datasets, DeepCrack, CFD, Cracktree200 and CRACK500, which achieves ODS values as 87.12%, 83.96%, 82.66%, 81.35% and OIS values as 87.55%, 84.82%, 83.56% and 82.98%. Compared with HED, RCF, DeepCrackT, FPHBN, ResNet-152 and DeepCrack, the ODS value performance improvement made in our method is 1.21%, 3.35%, 3.07%, 3.36%, 4.79% and 1% on DeepCrack dataset. Sufficient experimental statistics certificate that our proposed method outperforms other state-of-the-art crack detection, edge detection and image segmentation methods. [ABSTRACT FROM AUTHOR]

With the rapid development of the wind energy industry, there is an increasing concern about operation safety and reliability of high-strength wind turbine bolts. The aim of this paper is to monitor the strain change around the cracks in wind turbine bolts by means of fiber Bragg grating (FBG) sensors for crack detection. Firstly, the strain distributions of wind turbine bolts’ cracks with different locations and angles in the service condition are simulated using finite element analysis (FEA). Then, three-point grating string FBG sensors were pasted on the surface of wind turbine bolts with fatigue cracks to monitor the strain changes around the cracks in real time. By analysing the monitored strain data elaborately, the location of the crack on the bolt surface was successfully detected by identifying the location of the maximum strain detected by FBG sensors. In addition, the strain distributions in the vicinity area of the crack at different angles (0°, 45° and 90°) were also monitored and analysed in depth. The different types of crack angles could be distinguished based on of different strain distribution of the vicinity of the crack tip at different angles. The experimental results show that the FBG sensing technology has a high degree of sensitivity and accuracy in crack detection of high-strength wind turbine bolts. [ABSTRACT FROM AUTHOR]

A robust wood material crack detection algorithm, sensitive to small targets, is indispensable for production and building protection. However, the precise identification and localization of cracks in wooden materials present challenges owing to significant scale variations among cracks and the irregular quality of existing data. In response, we propose a crack detection algorithm tailored to wooden materials, leveraging advancements in the YOLOv8 model, named ICDW-YOLO (improved crack detection for wooden material-YOLO). The ICDW-YOLO model introduces novel designs for the neck network and layer structure, along with an anchor algorithm, which features a dual-layer attention mechanism and dynamic gradient gain characteristics to optimize and enhance the original model. Initially, a new layer structure was crafted using GSConv and GS bottleneck, improving the model's recognition accuracy by maximizing the preservation of hidden channel connections. Subsequently, enhancements to the network are achieved through the gather–distribute mechanism, aimed at augmenting the fusion capability of multi-scale features and introducing a higher-resolution input layer to enhance small target recognition. Empirical results obtained from a customized wooden material crack detection dataset demonstrate the efficacy of the proposed ICDW-YOLO algorithm in effectively detecting targets. Without significant augmentation in model complexity, the mAP50–95 metric attains 79.018%, marking a 1.869% improvement over YOLOv8. Further validation of our algorithm's effectiveness is conducted through experiments on fire and smoke detection datasets, aerial remote sensing image datasets, and the coco128 dataset. The results showcase that ICDW-YOLO achieves a mAP50 of 69.226% and a mAP50–95 of 44.210%, indicating robust generalization and competitiveness vis-à-vis state-of-the-art detectors. [ABSTRACT FROM AUTHOR]

This study is a first attempt to provide an effective tool for crack prediction in beam-type structures using the peridynamics (PD) theory and metaheuristic-based optimization. The norm of the difference between the response of the tested beam and that of the trial model (analyzed using PD), is minimized to find the position and depth of crack. Model calibration is discussed thoroughly. Five well-known metaheuristics as well as an upgraded charged system search algorithm are considered. Results reveal that the proposed procedure can effectively localize and detect the crack severity, even in noise contaminated cases. [ABSTRACT FROM AUTHOR]

Technology offers a lot of potential that is being used to improve the integrity and efficiency of infrastructures. Crack is one of the major concerns that can affect the integrity or usability of any structure. Oftentimes, the use of manual inspection methods leads to delays which can worsen the situation. Automated crack detection has become very necessary for efficient management and inspection of critical infrastructures. Previous research in crack detection employed classification and localization-based models using Deep Convolutional Neural Networks (DCNNs). This study suggests and compares the effectiveness of transfer learned DCNNs for crack detection as a classification model and as a feature extractor to overcome this restriction. The main objective of this paper is to present various methods of crack detection on surfaces and compare their performance over 3 different datasets. Experiments conducted in this work are threefold: initially, the effectiveness of 12 transfer learned DCNN models for crack detection is analyzed on three publicly available datasets: SDNET, CCIC and BSD. With an accuracy of 53.40%, ResNet101 outperformed other models on the SDNET dataset. EfficientNetB0 was the most accurate (98.8%) model on the BSD dataset, and ResNet50 performed better with an accuracy of 99.8% on the CCIC dataset. Secondly, two image enhancement methods are employed to enhance the images and are transferred learned on the 12 DCNNs in pursuance of improving the performance of the SDNET dataset. The results from the experiments show that the enhanced images improved the accuracy of transfer-learned crack detection models significantly. Furthermore, deep features extracted from the last fully connected layer of the DCNNs are used to train the Support Vector Machine (SVM). The integration of deep features with SVM enhanced the detection accuracy across all the DCNN-dataset combinations, according to analysis in terms of accuracy, precision, recall, and F1-score. [ABSTRACT FROM AUTHOR]

We present a theoretical study on the magnetic signals of structural surface defects like cracks or indents combined with inhomogeneities on the surface or subsurface inclusions of soft ferromagnetic metals like body-centred cubic Fe or amorphous CoFeB. We discuss limits of early detection of small surface defects on the basis of calculated magnetic stray fields few tens of nm above the surface. The considered surface imperfections have extensions of a few nm which correspond to low multiples of the magnetic exchange lengths of Fe or CoFeB. The detection of such small inhomogeneities requires that the sensor is about as close to the surface as the size of the inhomogeneity is. Furthermore, the step width of a scanning sensor must be of the same size as well. Both these requirements may be fulfilled for instance by scanning microscopy with diamond nitrogen-vacancy-centre quantum sensors. [ABSTRACT FROM AUTHOR]

Structural health monitoring for roads is an important task that supports inspection of transportation infrastructure. This paper explores deep learning techniques for crack detection in road images and proposes an automatic pixel-level semantic road crack image segmentation method based on a Swin transformer. This method employs Swin-T as the backbone network to extract feature information from crack images at various levels and utilizes the texture unit to extract the texture and edge characteristic information of cracks. The refinement attention module (RAM) and panoramic feature module (PFM) then merge these diverse features, ultimately refining the segmentation results. This method is called FetNet. We collect four public real-world datasets and conduct extensive experiments, comparing FetNet with various deep-learning methods. FetNet achieves the highest precision of 90.4%, a recall of 85.3%, an F1 score of 87.9%, and a mean intersection over union of 78.6% on the Crack500 dataset. The experimental results show that the FetNet approach surpasses other advanced models in terms of crack segmentation accuracy and exhibits excellent generalizability for use in complex scenes. [ABSTRACT FROM AUTHOR]

Ferromagnetic pipes are widely used in the oil and gas industry. They are subject to cracks due to corrosion, pressure, and fatigue. It is significant to detect cracks for the safety of pipes. A residual magnetic field testing (RMFT) system is developed for crack detection in ferromagnetic pipes. Based on this background, a detection probe based on an array of tunneling magneto-resistive (TMR) sensors and permanent magnets is exploited. The probe is able to partially magnetize the pipe wall and collect magnetic signals simultaneously. First, a theoretical analysis of RMFT is presented. The physics principle of RMFT is introduced, and a finite element model is built. In the finite element simulations, the effects of the crack length and depth on the RMFT signal are analyzed, and the signal characteristics are selected to represent the crack size. Next, the validated experiments are conducted to demonstrate the feasibility of the proposed RMFT method in this paper. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative approach for detecting and quantifying concrete cracks using an adaptive threshold method based on Median Absolute Deviation (MAD) in images. The technique applies limited pre-processing steps and then dynamically determines a threshold adapted for each sub-image depending on the greyscale distribution of the pixels, resulting in tailored crack segmentation. The edges of the crack are obtained using the Laplace edge detection method, and the width of the crack is obtained for each centreline point. The method's performance is measured using the Probability of Detection (POD) curves as a function of the actual crack size, revealing remarkable capabilities. It was found that the proposed method could detect cracks as narrow as 0.1 mm, with a probability of 94% and 100% for cracks with larger widths. It was also found that the method has higher accuracy, precision, and F2 score values than the Otsu and Niblack methods. [ABSTRACT FROM AUTHOR]

The detection of cracks is extremely important for maintenance of concrete structures. Deep learning-based segmentation models have achieved high accuracy in crack segmentation. However, mainstream crack segmentation models have very high computational complexity, and therefore cannot be used in portable crack detection equipment. To address this problem, a knowledge distilling structure is designed by us. In this structure, a large teacher model named TBUNet is proposed to transfer crack knowledge to a student model with symmetry structure named ULNet. In the TBUNet, stacked transformer modules are used to capture dependency relationships between different crack positions in feature maps and achieve contextual awareness. In the ULNet, only a tiny U-Net with light-weighted parameters is used to maintain very low computational complexity. In addition, a mixed loss function is designed to ensure detail and global features extracted by the teacher model are consistent with those of the student model. Our designed experiments demonstrate that the ULNet can achieve accuracies of 96.2%, 87.6%, and 75.3%, and recall of 97.1%, 88.5%, and 76.2% on the Cracktree200, CRACK500, and MICrack datasets, respectively, which is 4–6% higher than most crack segmentation models. However, the ULNet only has a model size of 1 M, which is suitable for use in portable crack detection equipment. [ABSTRACT FROM AUTHOR]

This paper introduces a deep learning approach for accurately predicting the position and size of cracks in steam turbine blades using frequency response function data obtained from finite element analysis. Training deep neural networks for crack prediction necessitates an extensive dataset comprising various crack conditions, along with corresponding position and size information. However, obtaining sufficient high-quality and well-balanced experimental data for diverse crack conditions poses challenges. To address this issue, we leverage finite element analysis techniques to effectively amass a large volume of training data. Our methodology involves frequency response analysis, incorporating a crack modeling approach based on node sharing elimination and model order reduction (MOR). Automating this process enables us to efficiently generate a diverse dataset of crack blade frequency response data in a short timeframe, focusing on cracks in a single blade. The collected data is then transformed into two-dimensional image data, including magnitude similarity maps and magnitude frequency response maps, which serve as training data for our convolutional neural network (CNN) model. Evaluation of the CNN model's performance and accuracy for crack position and size prediction highlights its remarkable precision in determining the initiation position and size of blade cracks. Utilizing the proposed prediction model, there was a 95 % probability of accurately predicting the crack position within 3.1 % of the entire airfoil area. Furthermore, successful crack size predictions were achieved for approximately 96.4 % of the entire test dataset within an error range of ±1 mm. This underscores the potential capability of neural network training and crack prediction by leveraging training data derived from the finite element analysis and MOR. Our approach demonstrates promising implications for enhancing steam turbine blade maintenance and structural integrity assessment, as it streamlines data acquisition and provides an efficient solution for predicting critical crack characteristics. [ABSTRACT FROM AUTHOR]

The degradation of road pavements due to environmental factors is a pressing issue in infrastructure maintenance, necessitating precise identification of pavement distresses. The pavement condition index (PCI) serves as a critical metric for evaluating pavement conditions, essential for effective budget allocation and performance tracking. Traditional manual PCI assessment methods are limited by labor intensity, subjectivity, and susceptibility to human error. Addressing these challenges, this paper presents a novel, end-to-end automated method for PCI calculation, integrating deep learning and image processing technologies. The first stage employs a deep learning algorithm for accurate detection of pavement cracks, followed by the application of a segmentation-based skeleton algorithm in image processing to estimate crack width precisely. This integrated approach enhances the assessment process, providing a more comprehensive evaluation of pavement integrity. The validation results demonstrate a 95% accuracy in crack detection and 90% accuracy in crack width estimation. Leveraging these results, the automated PCI rating is achieved, aligned with standards, showcasing significant improvements in the efficiency and reliability of PCI evaluations. This method offers advancements in pavement maintenance strategies and potential applications in broader road infrastructure management. [ABSTRACT FROM AUTHOR]

In view of the low identification accuracy of the crack-detection technology of asphalt pavement under the current conditions of complex pavement (subject to strong light, water on the road, debris, and so on), an asphalt pavement crack-detection algorithm based on improved YOLOv5s was proposed by building the data set for asphalt pavement cracks. The first step was to make the following improvements to the original YOLOv5s model according to the characteristics of the asphalt pavement crack data set: The k -means++ algorithm was used to recluster the anchor points of the crack data set, and the initial anchor frame matching the crack characteristics of the asphalt pavement was obtained to replace the default initial anchor frame in the YOLOv5 original model. In the prediction part of the original model, the Convolutional Block Attention Module (CBAM) was added in the order of first channel and then space to improve the detection ability of the model to small cracks. The CIoU_Loss function was used as the regression loss function of the model to replace the GIoU_Loss function in the original model to improve the positioning accuracy of the anchor frame. The second step was to perform an ablation experiment on the improved YOLOv5s model. This would prove that each improvement scheme could increase detection ability without conflict. The final step was to compare the improved YOLOv5s model with various classic target detection models in the data set of this paper: the Crack Forest Data set (CFD), the Crack500 data set, and the Crack200 data set. The results showed that the detection of the improved YOLOv5s model on each data set was better than other target detection models. The mAP@0.5 and mAP@[0.5:0.95] of this model on the data set of this paper were 90.58% and 56.08%, respectively, which were much higher than other target detection models. These findings indicate that the improved YOLOv5s model had better detection under complex pavement conditions and could provide a theoretical basis for the automatic detection of asphalt pavement cracks. [ABSTRACT FROM AUTHOR]

Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method of ensuring the safety and health of engineered structures is the prompt detection of cracks. Image threshold segmentation based on machine vision is a crucial technology for crack detection. Threshold segmentation can separate the crack area from the background, providing convenience for more accurate measurement and evaluation of the crack condition and location. The segmentation of cracks in complex scenes is a challenging task, and this goal can be achieved by means of multilevel thresholding. The arithmetic-geometric divergence combines the advantages of the arithmetic mean and the geometric mean in probability measures, enabling a more precise capture of the local features of an image in image processing. In this paper, a multilevel thresholding method for crack image segmentation based on the minimum arithmetic-geometric divergence is proposed. To address the issue of time complexity in multilevel thresholding, an enhanced particle swarm optimization algorithm with local stochastic perturbation is proposed. In crack detection, the thresholding criterion function based on the minimum arithmetic-geometric divergence can adaptively determine the thresholds according to the distribution characteristics of pixel values in the image. The proposed enhanced particle swarm optimization algorithm can increase the diversity of candidate solutions and enhance the global convergence performance of the algorithm. The proposed method for crack image segmentation is compared with seven state-of-the-art multilevel thresholding methods based on several metrics, including RMSE, PSNR, SSIM, FSIM, and computation time. The experimental results show that the proposed method outperforms several competing methods in terms of these metrics. [ABSTRACT FROM AUTHOR]

The availability of carbon nanotube (CNT)-based polymer composites allows the development of surface-attached self-sensing crack sensors for the structural health monitoring of reinforced concrete (RC) structures. These sensors are fabricated by integrating CNTs as conductive fillers into polymer matrices such as polyurethane (PU) and can be applied by coating on RC structures before the composite hardens. The principle of crack detection is based on the electrical change characteristics of the CNT-based polymer composites when subjected to a tensile load. In this study, the electrical conductivity and electro-mechanical/environmental characterization of smart skin fabricated with various CNT concentrations were investigated. This was performed to derive the tensile strain sensitivity of the smart skin according to different CNT contents and to verify their environmental impact. The optimal CNT concentration for the crack detection sensor was determined to be 5 wt% CNT. The smart skin was applied to an RC structure to validate its effectiveness as a crack detection sensor. It successfully detected and monitored crack formation and growth in the structure. During repeated cycles of crack width variations, the smart skin also demonstrated excellent reproducibility and electrical stability in response to the progressive occurrence of cracks, thereby reinforcing the reliability of the crack detection sensor. Overall, the presented results describe the crack detection characteristics of smart skin and demonstrate its potential as a structural health monitoring (SHM) sensor. [ABSTRACT FROM AUTHOR]

In concrete structures, surface cracks are an important indicator for assessing the durability and serviceability of the structure. Existing convolutional neural networks for concrete crack identification are inefficient and computationally costly. Therefore, a new Cross Swin transformer-skip (CSW-S) is proposed to classify concrete cracks. The method is optimized by adding residual links to the existing Cross Swin transformer network and then trained and tested using a dataset with 17,000 images. The experimental results show that the improved CSW-S network has an extended range of extracted image features, which improves the accuracy of crack recognition. A detection accuracy of 96.92% is obtained using the trained CSW-S without pretraining. The improved transformer model has higher recognition efficiency and accuracy than the traditional transformer model and the classical CNN model. [ABSTRACT FROM AUTHOR]

Magnetic field measurements play a vital role in various industries, particularly in the detection of cracks using magnetic field images, also known as magnetic field leakage testing. This paper presents an approach to automate the extraction of crack signals in magnetic field imaging by using neural networks. The proposed method relies on simulation-based training using the lightweight Python library Magpylib to calculate the three-dimensional static magnetic field of permanent magnets with surface defects. This approach has numerous advantages. It allows control of training data set variance by tuning simulation input parameters such as sample magnetization, measurement parameters, and defect properties to cover a wide range of cracks in size and position. Starting data acquisition before system operation allows investigating potential changes in sample shape or measurement parameters. Importantly, simulation-based data generation eliminates the need for physical measurements, leading to significant time savings. The study presents and discusses results obtained on two different ferromagnetic samples with surface cracks, a hollow cylinder and a steel sheet. [ABSTRACT FROM AUTHOR]

Crack detection is integral in civil infrastructure maintenance, with automated robots for detailed inspections and repairs becoming increasingly common. Ensuring fast and accurate crack detection for autonomous vehicles is crucial for safe road navigation. In these fields, existing detection models demonstrate impressive performance. However, they are primarily optimized for clear weather and struggle with occlusions and brightness variations in adverse weather conditions. These problems affect automated robots and autonomous vehicle navigation that must operate reliably in diverse environmental conditions. To address this problem, we propose Auxcoformer, designed for robust crack detection in adverse weather conditions. Considering the image degradation caused by adverse weather conditions, Auxcoformer incorporates an auxiliary restoration network. This network efficiently restores damaged crack details, ensuring the primary detection network obtains better quality features. The proposed approach uses a non-local patch-based 3D transform technique, emphasizing the characteristics of cracks and making them more distinguishable. Considering the connectivity of cracks, we also introduce contrastive patch loss for precise localization. Then, we demonstrate the performance of Auxcoformer, comparing it with other detection models through experiments. [ABSTRACT FROM AUTHOR]

Accurate and rapid crack detection has been one of the most important steps in duck eggs before processing. The shell of duck eggs may crack, particularly when subjected to the vibration, extrusion, and collision during production, transportation, packaging and storage. The microorganism can then invade through the crack, easy to cause the deterioration of duck eggs. Furthermore, the damage of one duck egg can greatly affect the processing requirements of the whole batch. However, manual detection cannot fully meet the large-scale production in recent years, due to the unstable accuracy rate, low efficiency and industrialization in the poultry egg industry at present. In this study, a lightweight detection was proposed for the multi-column cracks of duck eggs using improved YOLOv5l (you only look once version5 large). The better detection of duck egg was achieved in the simple, small amount of calculation and storage. A LED light was used to illuminate the duck egg under dark conditions. There was the difference of light transmittance between cracked eggshell and intact eggshell. Specifically, the Ghost_conv module was introduced to replace the original convolution in YOLOv5. The calculation and parameters of model were greatly reduced to increase the training speed with more lightweight. In addition, ECA (efficient channel attention) attention mechanism was added to the backbone Network and BIFPN (bi-directional feature pyramid network). The effective information was detected using the attention mechanism. Among them, the BIFPN was used to extract and fuse some features at different scales. The accuracy of model was improved to reduce the missing and wrong detection. At the same time, the fusion of CIoU and α-IoU loss function was used to replace the original GIoU function of YOLOv5, thus accelerating the regression prediction. A dataset of duck egg was obtained with 892 images, where a camera was used to capture duck eggs rolling on a conveyor. The dataset was then enhanced by random cropping, adding noise and mixup, in order to further improve the generalization and robustness of the model. The self-developed dataset of duck egg cracks was utilized to verify the detection performance of the improved model. The results show that the improved YOLOv5l network model shared the high performance. The detection accuracy of the improved model on the test set was 93.8%, which was 6.3 percentage points higher than that of the original YOLOv5l model. The number of parameters, floating point arithmetic and the occupied storage was reduced by 30.6%, 39.4% and 34.6%, respectively. The frame rate of detection was 28.954 frames /s, which was only 3.824 frames /s lower than the original YOLOv5l model. The detection accuracy of the improved YOLOv5l increased by 13.1, 12.5 and 8.2 percentage points, respectively, compared with the common target detection networks, such as single shot MultiBox detector (SSD), YOLOv4, and faster-RCNN (faster region convolutional neural networks). The high-precision detection with the low hardware resources can be expected to provide the promising solutions and technical applications. [ABSTRACT FROM AUTHOR]

Crack detection is a long-standing topic in structural health monitoring. Conventional damage detection techniques rely on intensive, time-consuming, resource-intensive intervention. The current trend of crack detection emphasizes using deep neural networks to build an automated pipeline from measured signals to damaged areas. This work focuses on the seismic-wave-based technique of crack detection for plate structures. Previous work proposed an encoder–decoder network to extract crack-related wave patterns from measured wave signals and predict crack existence on the plate. We extend previous work with extensive experiments on different network components and a data preprocessing strategy. The proposed methods are tested on an expanded crack detection dataset. We found that a robust backbone network, such as Densely Connected Convolutional Network (DenseNet) can effectively extract the features characterizing cracks of wave signals, and by using the reference wave field for normalization, the accuracy of detecting small cracks can be further improved. [ABSTRACT FROM AUTHOR]

Curvature-based damage detection has been previously applied to identify damage in concrete structures, but little attention has been given to the capacity of this method to identify distributed damage in multiple damage zones. This study aims to apply for the first time an enhanced existing method based on modal curvature analysis combined with wavelet transform curvature (WTC) to identify zones and highlight the damage zones of a beam made of ultra-high-performance fiber-reinforced concrete (UHPFRC), a construction material that is emerging worldwide for its outstanding performance and durability. First, three beams with a 2 m span of UHPFRC material were cast, and damaged zones were created by sawing. A reference beam without cracks was also cast. The free vibration responses were measured by 12 accelerometers and calculated by operational modal analysis. Moreover, for the sake of comparison, a finite element model (FEM) was also applied to two identical beams to generate numerical acceleration without noise. Second, the modal curvature was calculated for different modes for both experimental and FEM-simulated acceleration after applying cubic spline interpolation. Finally, two damage identification methods were considered: (i) the damage index (DI), based on averaging the quadratic difference of the local curvature with respect to the reference beam, and (ii) the WTC method, applied to the quadratic difference of the local curvature with respect the reference beam. The results indicate that the developed coupled modal curvature WTC method can better identify the damaged zones of UHPFRC beams. [ABSTRACT FROM AUTHOR]

Cracks on highway pavement surfaces are among the most critical problems affecting expressway maintenance. A pan/tilt/zoom (PTZ) camera‐based crack detection method was proposed, owing to the intelligent traffic system (ITS) development of an expressway network in China. However, the quality of the crack image decreased at distant PTZ camera distances, resulting in a low mean average precision (MVP) for crack detection, which is difficult to implement in civil engineering. A weighted superposition‐based crack enhancement algorithm for crack detection was proposed to enhance crack images collected by PTZ cameras. Compared with the poor generalization ability of using a single image enhancement method, this method combines the advantages and characteristics of multiple image enhancement methods, thereby ensuring the quality of crack images. This enhances the visual characteristics of the cracks to adapt to various illuminations, thereby improving the crack detection rate and providing scientific and technological reserves for its application in highway maintenance engineering. Further comparison tests on crack detection for pavement crack images collected from the China G4/Jingshi Highway demonstrated that the proposed method was efficient in detecting is efficiently to detect cracks, achieving 96% MAP and 94% F1 values after enhancing the crack images, thereby providing an objective and promising means of crack detection using PTZ cameras. [ABSTRACT FROM AUTHOR]

In the field of sewage pipeline inspection, the Pipeline Assessment Certification Program (PACP) requires encoding pipe cracks at the subcategory level in practical applications. This study seeks to address the need to improve the accuracy of the crack pixel-level segmentation and subcategory classification problem based on sewer pipe inspection video images. The proposed framework consists of a feature extraction module, a multiscale feature fusion module, a mask-guided attention module, and a subcategory classification module. First, the multiscale feature fusion mechanism is used to integrate the convolution features of different scales to obtain the convolution features rich in both semantic and detailed information, which can improve the characterization ability of cracks and achieve accurate crack detection. Then, mask-guided attention is used to refine features and eliminate background noise, enhancing the feature representation for crack pixels and reducing the ambiguity of the fine-grained crack classification task as well. Finally, the subcategory classification result is obtained based on the feature maps with erased background clutter. The experimental results show that the proposed method achieved F1-scores of 97%, 94%, and 95% for longitudinal, circumferential, and multiple cracks, respectively. The main contribution of the study is the formulation of a multitask collaborative learning framework, which combines pixel-level segmentation and global image-level classification annotation to achieve a desirable performance of sewer crack detection and classification at a subcategory level. [ABSTRACT FROM AUTHOR]

In a seismic context, it is fundamental to deploy distributed sensor networks for Structural Health Monitoring (SHM). Indeed, regularly gathering data from a structure/infrastructure gives insight on the structural health status, and Artificial Intelligence (AI) technologies can help in exploiting this information to generate early warnings useful for decision-making purposes. With a perspective of developing a remote monitoring platform for the built environment in a seismic context, the authors tested self-sensing concrete beams in loading tests, focusing on the measured electrical impedance. The formed cracks were objectively assessed through a vision-based system. Also, a comparative analysis of AI-based and statistical prediction methods, including Prophet, ARIMA, and SARIMAX, was conducted for predicting electrical impedance. Results show that the real part of electrical impedance is highly correlated with the applied load (Pearson's correlation coefficient > 0.9); hence, the piezoresistive ability of the manufactured specimens has been confirmed. Concerning prediction methods, the superiority of the Prophet model over statistical techniques was demonstrated (Mean Absolute Percentage Error, MAPE < 1.00%). Thus, the exploitation of electrical impedance sensors, vision-based systems, and AI technologies can be significant to enhance SHM and maintenance needs prediction in the built environment. [ABSTRACT FROM AUTHOR]

Crack detection plays a critical role in ensuring road safety and maintenance. Traditional, manual, and semi-automatic detection methods have proven inefficient. Nowadays, the emergence of deep learning techniques has opened up new possibilities for automatic crack detection. However, there are few methods with both localization and segmentation abilities, and most perform poorly. The consistent nature of pavement over a small mileage range gives us the opportunity to make improvements. A novel data-augmentation strategy called CrackMover, specifically tailored for crack detection methods, is proposed. Experiments demonstrate the effectiveness of CrackMover for various methods. Moreover, this paper presents a new instance segmentation method for crack detection. It adopts a redesigned backbone network and incorporates a cascade structure for the region-based convolutional network (R-CNN) part. The experimental evaluation showcases significant performance improvements achieved by these approaches in crack detection. The proposed method achieves an average precision of 33.3%, surpassing Mask R-CNN with a Residual Network 50 backbone by 8.6%, proving its effectiveness in detecting crack distress. [ABSTRACT FROM AUTHOR]