Search

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]

Abstarct: Introduction: The frequency response function of a structure is an important attribute that should be gathered for vibration-based damage detection in structural health monitoring (SHM). However, to collect a sufficient number of frequency response functions for the damage detection problem, a large number of sensors and exciters is required. Using smart materials such as piezoelectric material leads to the electro-mechanical impedance (EMI) technique, a powerful tool for remote and real-time performing the SHM. However, the EMI technique is still limited to a small area of damage monitoring and is efficient only in the very high-frequency range. The present paper is devoted to modifying the EMI technique by using the full-length distributed sensor in combination with point mechanical excitation instead of the impedance transducer employed in the EMI technique. This leads to a novel concept of electro-mechanical receptance (EMR) proposed for crack monitoring overlength of beam structure and conducting SHM in the lowest frequency range. Method: The electro-mechanical receptance defined as elelectric charge generated in the piezoelectric layer under concentrated unit mechanical excitation is conducted by analytical method. So, an explicit expression of the electro-mechanical receptance is derived and used for extracting so-called spectral damage index and spectral assurance criterion that are then employed for numerical analysis of EMR’s sensitivity to crack and establishing diagnostic database. Crack detection problem is solved by the contour method using constructed diagnostic database. Results: A concept of electro-mechanical receptance is proposed and its analytical expression has been derived for cracked Timoshenko beam integrated with a piezoelectric layer. It was demonstrated that the proposed electro-mechanical receptance is much more sensitive to crack than modal parameters in the lowest frequency range and this high sensitivity is maintained only for piezoelectric layer thickness less than 15% beam thickness. A so-called spectral assurance criterion that is extracted from electro-mechanical receptance provides an efficient indicator for crack detection in beam structures by measurements of the electro-mechanical receptance. Numerical results show that small crack of 5% beam thickness could be consistently identified from measured electro-mechanical receptance. Conclusion: The coupled electro-mechanical dynamic behavior of a cracked Timoshenko beam bonded with a piezoelectric layer has been analytically studied. An analytical expression of electric charge produced in the piezoelectric layer under concentrated unit mechanical excitation was derived and called herein electro-mechanical receptance for the integrated beam structure. The proposed electro-mechanical receptance is much more sensitive to crack than modal parameters and more easily measured in the lowest frequency range compared to the electro-mechanical impedance. The so-called spectral assurance criterion extracted from measured electro-mechanical receptance enables consistently detecting small cracks of 5% beam thickness. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Automatic crack detection is a key task to ensure the quality of concrete pavement and improve the efficiency of pavement maintenance. To address the problem of failing to adaptively combine multi-scale spatial information and the loss of crack detail information in crack detection, a network model with hierarchical feature fusion and adaptive receptive field has been proposed. Firstly, the improved SKNet serves as the backbone network for extracting multi-scale features. Subsequently, the corresponding attention mechanism is introduced to optimize the side output, enhancing attention to the crack location and channel information. Finally, we propose a method that fuses spatial separable convolution and attention mechanism, and design a spatial attention fusion module to restore more crack details. The side network integrates low-level features and high-level features at multiple levels to assist in obtaining the final prediction map. To verify the validity of the proposed method, we evaluate it on three publicly available crack datasets: DeepCrack, CFD and Crack500, achieving F-score ( F 1 ) values of 87.20%, 63.53% and 62.26%, respectively. [ABSTRACT FROM AUTHOR]

Roads frequently experience cracks. It adversely impact the safe passage of vehicles and pedestrians, and have the potential to alter the road’s structure. To address this issue, we propose a novel crack detection network. The network constructs multi-channel attention and enhanced information interaction mechanisms to capture more granular semantic information. In our network, each convolutional layer is followed by a convolution combining asymmetric convolutions and criss-cross attention to enhance the feature maps post-convolution. This is followed by spatial and channel reconstruction convolutions and shuffle attention to optimize the generated side-output features. By extensively mining features from the deep network and ingeniously integrating bottom-level and top-level features through a new feature fusion module. The network achieves precise crack prediction results. Extensive experiments on the general-purpose crack image datasets Crack500, CFD and DeepCrack demonstrate the model’s effectiveness. In these three datasets, F1-score values of 0.734, 0.635, and 0.881, MIoU values of 0.773, 0.726 and 0.888. [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]

Owing to the long-standing statement that natural frequency-based crack detection is not sensitive enough to localised damage to identify small cracks, many natural frequency-based crack detection methods are validated by detecting cracks of moderate size. However, a direct comparison between the crack severity causing a measurable natural frequency change and the crack severity reaching the initiation of crack propagation or leading to brittle fracture is constantly ignored. Without this understanding, it is debatable whether the presented crack detection methods are feasible in practical situations. Through natural frequency calculation and linear elastic fracture mechanics, this study is dedicated to filling the above gap in knowledge. To directly utilize the solution of stress intensity factor, common fracture toughness test specimens featuring a single-edge crack are used. These specimens are essentially cracked rectangular plates under uniform uniaxial tension. Considering the stress resultants obtained via the extended finite element method, the natural frequency of the loaded cracked plates is calculated using the Rayleigh–Ritz method incorporating corner functions. In addition, assuming the specimens as structural components under remote uniform tension, the development of critical load versus crack length is derived based on the solution of the stress intensity factor. Thus, critical crack lengths corresponding to a series of safety factors are obtained by equating service load with critical load. After obtaining natural frequencies of the cracked plates with critical crack lengths, the natural frequency drop caused by a critical crack can be computed. Hence, the critical crack length can be compared with the crack length when the frequency drop is measurable. It is found that the brittleness of the employed metals plays a vital role in the feasibility of natural frequency-based crack detection. [ABSTRACT FROM AUTHOR]

As the expected lifespans of structures and road approaches, as well as the importance of road maintenance, increase globally, safety inspections have emerged as a crucial task. Nonetheless, the existing crack detection models focus on multi-scale feature loss and performance degradation in learning various types of cracks. We propose the Multi-Scale Parallel Attention U-Net (MSP U-Net) as a network designed for low-resolution images that considers the irregular characteristics of cracks. MSP U-Net applies a large receptive field flock to an attention U-Net, minimizing feature loss across multiple scales. Using the Crack500 dataset, our network achieved a mean intersection of union (mIoU) of 0.7752, outperforming the existing methods on low-resolution images. [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]

Surface cracks are one of the primary defects in nuclear fuel pellets, posing a significant hazard to nuclear safety production. Deep learning-based methods recently developed for crack detection are typically trained using a supervised learning, with detection performance dependent on pixel-level annotations, which suffer from high labeling costs and low efficiency. To address this issue, we propose a Weakly Supervised Crack Detection (WSCD) network for surface crack detection of nuclear fuel pellets. The method adopts bounding-box annotations instead of pixel-level annotations, and rapidly generates pixel-level pseudo-labels using the designed Local Fusion Segmentation (LFS) module. Leveraging the Mask-RCNN network as the backbone, the network introduces spatial attention mechanisms to optimize feature extraction networks, enhancing the extraction capability of multi-scale crack features. Lastly, a novel loss function is optimized to address sample imbalance issues and expedite network convergence. Experimental results on the established crack dataset demonstrate that the proposed method improves labeling efficiency by approximately 20 times, achieving a segmentation accuracy (IoU) of 81.9%, which reaches 92.2% of the segmentation accuracy achieved by supervised learning and outperforms that the IoU (80.3%) of other supervised crack segmentation network for nuclear fuel pellets, and meeting the precision requirements of nuclear fuel pellets production lines. [ABSTRACT FROM AUTHOR]

Crack detection and measurement are essential for assessing the structural integrity of reinforced concrete (RC) structures, but challenges such as surface variability and class imbalance complicate accurate detection. This study introduces an approach integrating Convolutional Neural Networks (ConvNets), adaptive sliding windows, and DBSCAN-based semantic segmentation to address these challenges and enhance crack detection and quantification. The method was evaluated on various surface types, including painted masonry and concrete pavement, with a particular focus on overcoming class imbalance. To tackle this issue, the resampling (RS) technique was applied, achieving the best balance between precision and recall, with an F1 score of 0.836 during validation. The adaptive sliding window algorithm, optimized for lower magnification factors, further enhanced crack localization, improving IoU, recall, and precision. In semantic segmentation, the proposed method performed competitively on the DeepCrack dataset, achieving an IoU of 0.671, comparable to state-of-the-art models. Additionally, the measurement algorithm, which captures crack features such as length, width, and orientation, was tested on multiple surfaces. For painted masonry, it achieved a precision of 0.99, recall of 0.845, and IoU of 0.838, while for concrete pavement, it achieved a precision of 0.983, recall of 0.835, and IoU of 0.823. When applied to the DeepCrack dataset ground truth, it yielded a recall of 0.884, precision of 0.971, and IoU of 0.860. The results demonstrate the robustness and adaptability of this framework, offering an effective solution for automated crack detection and measurement across diverse surfaces. [ABSTRACT FROM AUTHOR]

With the rapid advancement of highway construction, the maintenance of highway infrastructure has become particularly vital. During highway maintenance, the effective detection of tiny road surface cracks helps to extend the lifespan of roads and enhance traffic efficiency and safety. To elevate the performance of existing road detection models, the CrackTinyNet (CrTNet) algorithm is specifically proposed for detecting tiny road surface cracks. This algorithm utilizes the novel BiFormer general visual transformer, designed expressly for tiny objects, and optimizes the loss function to a normalized Wasserstein distance loss function. It replaces traditional downsampling with Space‐to‐Depth Conv to prevent the excessive loss of tiny object information in the network structure. To highlight the model's advantage in detecting tiny road cracks, ablation experiments and comparison trials were conducted with mainstream deep learning models for crack detection. The results of the ablation experiments show that, compared to the baseline, CrTNet improved the Mean Average Precision (MAP) by 0.22. When compared to other network models suitable for road detection, these results exhibited an improvement of over 8.9%. In conclusion, the CrTNet proposed in this study enables a more accurate detection of tiny road cracks, playing a significant role in the advancement of intelligent traffic management. [ABSTRACT FROM AUTHOR]

The detection of cracks in structural facilities is of significant importance for infrastructure maintenance and public safety. However, recent huge computations of deep neural networks make it difficult to run them on real-time monitoring devices with limited computing and storage capacity. Additionally, in many low-quality environments such as dim, shadows, and blurriness, lightweight models often lack the performance required for accurate detection of cracks. In view of the above, we propose the structural re-parameterization enhanced lightweight segmentation model for low-quality environments crack detection. The model adopts a bilateral structure and uses different multi-scale network construction units for the two branches of model, allowing for better crack feature extraction from different perspective to enhance robustness and performance in low-quality environments. We design specific re-parameterization process for each branch's multi-scale construction unit to reduce the computational complexity of model inference, enabling the model to be better deployed on low resource monitoring devices for more efficient crack detection. A feature soft-selection mechanism is also proposed to better fuse the features of two branches. Experiment results on three concrete crack datasets indicate that our model can achieve good performance with lower complexity. Compared with the lightweight segmentation model BiSeNetV2, this model has nearly 52% and 43% less GFLOPs and MParams on the three datasets and achieves better performance. [ABSTRACT FROM AUTHOR]

This study introduces a state-of-the-art methodology for addressing crack segmentation challenges in structure health monitoring, a crucial concern in infrastructure maintenance. The main objective is to enhance real-time crack monitoring through a cutting-edge multimodal fusion approach that intricately combines a modified U-Net with transfer learning-based MobileNetV2. This integration strategically amalgamates spatial awareness and long-range dependency capture, resulting in an advanced model for crack segmentation. Thorough evaluations of a specialized crack detection data set underscore the efficacy of this integrated approach, positioning it as a reliable solution for real-time crack monitoring. Notably, the choice of MobileNetV2, recognized for its efficiency with the least parameters, contributes to the fusion's effectiveness. This study reveals superior performance, particularly when MobileNetV2 is integrated with U-Net, demonstrating enhanced accuracy and Intersection over Union (IOU) scores. [ABSTRACT FROM AUTHOR]

The You Only Look Once (YOLO) network is considered highly suitable for real-time object detection tasks due to its characteristics, such as high speed, single-shot detection, global context awareness, scalability, and adaptability to real-world conditions. This work introduces a comprehensive analysis of various YOLO models for detecting cracks in concrete structures, aiming to assist in the selection of an optimal model for future detection and segmentation tasks. The YOLO models are initially trained on a dataset containing both images with and without cracks, producing a generalized model capable of extracting abstract features beneficial for crack detection. Subsequently, transfer learning is employed using a dataset that reflects real-world conditions, such as occlusions, varying crack sizes, and rotations, to further refine the model. Crack detection in concrete remains challenging due to the wide variation in crack sizes, aspect ratios, and complex backgrounds. To achieve optimal performance, we test different versions of YOLO, a state-of-the-art single-shot detector, and aim to balance inference speed and mean average precision (mAP). Our results indicate that YOLOv10 demonstrates superior performance, achieving a mean average precision (mAP) of 74.52% with an inference time of 19.5 milliseconds per image, making it the most effective among the models tested. [ABSTRACT FROM AUTHOR]

Detecting structural cracks is critical for quality control and maintenance of industrial materials, ensuring their safety and extending service life. This study enhances the automation and accuracy of crack detection in microscopic images using advanced image processing and deep learning techniques, particularly the YOLOv8 model. A comprehensive review of relevant literature was carried out to compare traditional image-processing methods with modern machine-learning approaches. The YOLOv8 model was optimized by incorporating the Wise Intersection over Union (WIoU) loss function and the bidirectional feature pyramid network (BiFPN) technique, achieving precise detection results with mean average precision (mAP@0.5) of 0.895 and a precision rate of 0.859, demonstrating its superiority in detecting fine cracks even in complex and noisy backgrounds. Experimental findings confirmed the model's high accuracy in identifying cracks, even under challenging conditions. Despite these advancements, detecting very small or overlapping cracks in complex backgrounds remains challenging. Our future work will focus on optimizing and extending the model's generalisation capabilities. The findings of this study provide a solid foundation for automatic and rapid crack detection in industrial applications and indicate potential for broader applications across various fields. [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]

Advancements in image processing and deep learning offer considerable opportunities for automated defect assessment in civil structures. However, these systems cannot work interactively with human inspectors. Mixed reality (MR) can be adopted to address this by involving inspectors in various stages of the assessment process. This paper integrates You Only Look Once (YOLO) v5n and YOLO v5m with the Canny algorithm for real-time concrete crack detection and skeleton extraction with a Microsoft HoloLens 2 MR device. The YOLO v5n demonstrates a superior mean average precision (mAP) 0.5 and speed, while YOLO v5m achieves the highest mAP 0.5 0.95 among the other YOLO v5 structures. The Canny algorithm also outperforms the Sobel and Prewitt edge detectors with the highest F1 score. The developed MR-based system could not only be employed for real-time defect assessment but also be utilized for the automatic recording of the location and other specifications of the cracks for further analysis and future re-inspections. [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]

In recent times, the deployment of advanced structural health monitoring techniques has increased due to the aging infrastructural elements. This paper employed an enhanced You Only Look Once (YOLO) v4-tiny algorithm, based on the Crack Detection Model (CDM), to accurately identify and classify crack types in reinforced concrete (RC) members. YOLOv4-tiny is faster and more efficient than its predecessors, offering real-time detection with reduced computational complexity. Despite its smaller size, it maintains competitive accuracy, making it ideal for applications requiring high-speed processing on resource-limited devices. First, an extensive experimental program was conducted by testing full-scale RC members under different shear span (a) to depth ratios to achieve flexural and shear dominant failure modes. The digital images captured from the failure of RC beams were analyzed using the CDM of the YOLOv4-tiny algorithm. Results reveal the accurate identification of cracks formed along the depth of the beam at different stages of loading. Moreover, the confidence score attained for all the test samples was more than 95%, which indicates the accuracy of the developed model in capturing the types of cracks in the RC beam. The outcomes of the proposed work encourage the use of a developed CDM algorithm in real-time crack detection analysis of critical infrastructural elements. [ABSTRACT FROM AUTHOR]

As a critical component of the transportation system, the safety of bridges is directly related to public safety and the smooth flow of traffic. This study addresses the aforementioned issues by focusing on the identification of bridge structure deterioration and the updating of finite element models, proposing a systematic research framework. First, this study presents a preprocessing method for bridge point cloud data and determines the parameter ranges for key algorithms through parameter tuning. Subsequently, based on the massive point cloud data, this research explores and optimizes the methods for identifying bridge cracks and spatial deformations, significantly enhancing the accuracy and efficiency of identification. On this basis, the particle swarm optimization algorithm is employed to optimize the key parameters in crack detection, ensuring the reliability and precision of the algorithm. Additionally, the study summarizes the methods for detecting bridge structural deformations based on point cloud data and establishes a framework for updating the bridge model. Finally, by integrating the results of bridge crack and deformation detection and combining Bayesian model correction and adaptive nested sampling methods, this research sets up the process for updating finite element model parameters and applies it to the analysis of actual bridge point cloud data. [ABSTRACT FROM AUTHOR]

In the field of construction engineering, the cracking of concrete structures is a common engineering problem, which has a great impact on the overall stability and service life of the engineered structure. During structural repair, crack detection is the most critical step. Automatic detection significantly reduces the engineering cost and human factor error compared with manual detection. However, due to the changeable environment of the project site and different image specifications, using a single algorithm makes it difficult to balance high efficiency and high accuracy. In this study, we designed a combined recognition method including the region growth algorithm and machine learning regression that can achieve a tradeoff between accuracy and efficiency. Firstly, the regression method learns the image features of the dataset and the specific region growth threshold, and the regression function is trained by using the open-source dataset to determine the region growth threshold using the characteristics of the images included in the tests. The region growth algorithm is used to expand the threshold from the seed points of the image to obtain the crack recognition results. The results show that this method improves the accuracy of SSIM by 7% compared with the traditional region growth algorithm, and does not significantly increase the computational cost, with an increase of 0.78 s per photo process. Compared with the deep learning method, the recognition accuracy of SSIM is decreased by 5.96%, but it takes less resources and has high efficiency. [ABSTRACT FROM AUTHOR]

Concrete crack detection is a crucial task for the safety and durability of engineering structures. Extensive research has been conducted on deep-learning methods employing horizontal bounding boxes (HBBs) for crack detection. However, due to the inherently random distribution of concrete cracks, HBB-based methods often produce excessive overlaps and encompass extensive background regions, obstructing the effective interpretation and adaptation of the detection results. To address this issue and achieve efficient utilization of bounding box space for detecting cracks at any orientation, a rotated bounding box (RBB)-based method, that is, Rotated Faster R-CNN with a post processing strategy (RFR-P), was proposed. To realize this method, an RBB-based crack annotation strategy was introduced to standardize the annotation baseline for the evolutionarily established RBB-based crack detection dataset. Then, an RBB-based post-processing strategy was inventively developed to quantify the patterns of cracks with their corresponding rotation angles encompassing longitudinal cracks, transverse cracks, and diagonal cracks. Subsequently, experimental results showed that the RFR-P method provides more reasonable and elaborate detection results in terms of crack distribution patterns when compared to HBB-based methods. Based on the comprehensive consideration of evaluation metrics and detected results, it can be concluded that the RFR-P is aptly designed for detecting cracks at any rotation angle with relatively high accuracy. Finally, an RBB-based concrete crack detection platform was established to automatically detect in situ concrete bridge cracks for real-world applications. The proposed RFR-P model introduces a new perspective on crack detection methods and offers practical references for structural condition evaluation. [ABSTRACT FROM AUTHOR]

Crack detection or segmentation on concrete structures is a vital process in structural health monitoring (SHM). Though supervised machine learning techniques have gained tremendous success in this domain, data collection and annotation continue to be challenging. Image data collection is challenging, tedious, and laborious, including accessing representative datasets and manually labeling training data in the SHM domain. According to the literature, there are significant issues with the hand-annotation of image data. To address this gap, this paper proposes a two-stage weakly supervised learning framework utilizing a novel "crack attention network (CrANET)" with attention mechanism to detect and segment cracks on images with no human annotations in pixel-level labels. This framework classifies concrete surface images into crack or no-cracks and then uses gradient class activation mapping visualization to generate crack segmentation. Professionals and domain experts subsequently evaluate these segmentation results via a human expert validation study. As the literature suggests that weakly supervised learning is a limited practice in SHM, this research title will motivate researchers in SHM to research and develop a weakly supervised learning approach processing as state of the art. [ABSTRACT FROM AUTHOR]

Accurate crack detection is essential for structural damage assessment after earthquake disasters. However, due to the gap between the target domain of the detected structure and the source domain, it is challenging to achieve high-precision crack segmentation when performing crack detection based on deep learning (DL) in actual engineering. This article proposes a crack segmentation transfer learning method based on a self-supervised learning mechanism and a high-quality pseudo-label generation method, which can significantly improve the detection accuracy in the target domain without pre-made annotations. Besides, to improve the crack segmentation model's ability to extract local and global features, this article proposes a SegCrackFormer model, which embeds convolutional layers and multi-head self-attention modules. An experiment of the crack segmentation transfer learning method is performed on two open-source crack datasets, METU and Crack500, and a newly proposed LD dataset. The experimental results show that the crack segmentation transfer learning method proposed in this article can improve the mean intersection over union (mIoU) by 38.41% and 15.66% on the Crack500 and LD datasets, respectively. The proposed SegCrackFormer is evaluated through comparative experiments, which demonstrate its superiority over existing crack segmentation models on the METU dataset. Additionally, the proposed method is shown to require significantly less computational resources than other existing models, which highlights the potential of SegCrackFormer as a powerful and efficient model for crack segmentation in practical applications. [ABSTRACT FROM AUTHOR]

To determine the extent of pavement damage and forms of pavement distress, road pavement conditions must be precisely assessed. As a result, monitoring systems are regarded as an important stage in the maintenance procedure. In recent times, numerous investigations have been carried out to track the condition of pavement and monitor road surfaces. In the undertaken study, we have proposed a novel artificial intelligent (AI) and computer vision-enabled PavementCarevision 4.0 approach to detect and classify pavement health conditions i.e., defects. In this study, a customized pavement-2000 dataset has been designed which contains more than 2,000 images of a variety of pavement defects. In the initial phase, we pre-processed and enhanced pavement images using the customized adjustable linear contrast enhancement methodology. The enhanced pavement image samples were fed to the proposed customized YOLOV8 enabled PavementHealth 4.0 framework for pavement condition detection of a variety of pavement defects such as longitudinal cracks, alligator cracks, transverse cracks, and potholes. The proposed customized YOLOV8 enabled PavementHealth 4.0 framework has achieved an accuracy of 99.20 percent; an receiver operating characteristic (ROC) value of 0.98 and outperformed existing AI-based state-of-the-art methodologies such as pose NET, YOLOv7, YOLOv5, long short-term memory network (LSTM), Mask region-based convolutional neural network (R-CNN), and decision tree. [ABSTRACT FROM AUTHOR]

The automation of bridge disease detection necessitates the time-consuming, labor-intensive manual detection process and the limitations of traditional image segmentation methods, such as inadequate denoising effects and insufficient continuity in crack segmentation. This paper proposes a rapid detection and information feedback approach based on an enhanced Convolutional Neural Network (CNN) model to tackle these issues in bridge crack width measurement and information processing. To improve efficiency and accuracy in bridge safety monitoring, the training data is constructed by the bridge image library and network crack through the refined preprocessing and image segmentation techniques applied to these images, key features of cracks are identified and extracted to enhance the capability for crack identification. For crack assessment, the maximum internal tangent circle method is employed to accurately measure the width of bridge abutment cracks. The effectiveness of our model was verified through both fixed-point detection and Unmanned Aerial Vehicle (UAV) dynamic detection, ensuring comprehensive and accurate data collection. This dual validation strategy shows that our model substantiates the wide applicability across various scenarios, and the non-contact crack measurement technique achieves a precision of 0.01 mm, demonstrating the effectiveness and accuracy of this streamlined CNN model in accurately assessing crack width.

Abstract With the rapid advancement of highway construction, the maintenance of highway infrastructure has become particularly vital. During highway maintenance, the effective detection of tiny road surface cracks helps to extend the lifespan of roads and enhance traffic efficiency and safety. To elevate the performance of existing road detection models, the CrackTinyNet (CrTNet) algorithm is specifically proposed for detecting tiny road surface cracks. This algorithm utilizes the novel BiFormer general visual transformer, designed expressly for tiny objects, and optimizes the loss function to a normalized Wasserstein distance loss function. It replaces traditional downsampling with Space‐to‐Depth Conv to prevent the excessive loss of tiny object information in the network structure. To highlight the model's advantage in detecting tiny road cracks, ablation experiments and comparison trials were conducted with mainstream deep learning models for crack detection. The results of the ablation experiments show that, compared to the baseline, CrTNet improved the Mean Average Precision (MAP) by 0.22. When compared to other network models suitable for road detection, these results exhibited an improvement of over 8.9%. In conclusion, the CrTNet proposed in this study enables a more accurate detection of tiny road cracks, playing a significant role in the advancement of intelligent traffic management.

The importance of the parameters of any optimization algorithm, especially meta-heuristic algorithms that have been created to simplify the solution of optimization problems, is inevitable. The optimal values of these parameters, which generally depend on the specifics of the problem in question, have a significant impact on the performance of the mentioned algorithms and a better search of the solution space. Parameters selection of them will play an important role in performance and efficiency of the algorithms. This article examines the capability of various optimization algorithms and suggests dual hybrid optimization algorithms are named PSO-FA, PSO-GA, PSO-GWO, for solving the problem of computing the depth and location of cracks in cantilever beams. The performance of Particle swarm optimization (PSO), Genetic algorithm (GA), Grey wolf optimization (GWO), Firefly algorithm (FA), and hybrid of them base on PSO optimizer to determine the location and depth of crack for cantilever beam are proposed. These suggested algorithms are optimization algorithms based on intelligent optimization. So, the performance of these algorithms are analyzed when the control parameters vary.

Purpose – The purpose of this study is to study the quantitative evaluation method of contact wire cracks by analyzing the changing law of eddy current signal characteristics under different cracks of contact wire of high-speed railway so as to provide a new way of thinking and method for the detection of contact wire injuries of high-speed railway. Design/methodology/approach – Based on the principle of eddy current detection and the specification parameters of high-speed railway contact wires in China, a finite element model for eddy current testing of contact wires was established to explore the variation patterns of crack signal characteristics in numerical simulation. A crack detection system based on eddy current detection was built, and eddy current detection voltage data was obtained for cracks of different depths and widths. By analyzing the variation law of eddy current signals, characteristic parameters were obtained and a quantitative evaluation model for crack width and depth was established based on the back propagation (BP) neural network. Findings – Numerical simulation and experimental detection of eddy current signal change rule is basically consistent, based on the law of the selected characteristics of the parameters in the BP neural network crack quantitative evaluation model also has a certain degree of effectiveness and reliability. BP neural network training results show that the classification accuracy for different widths and depths of the classification is 100 and 85.71%, respectively, and can be effectively realized on the high-speed railway contact line cracks of the quantitative evaluation classification. Originality/value – This study establishes a new type of high-speed railway contact wire crack detection and identification method, which provides a new technical means for high-speed railway contact wire injury detection. The study of eddy current characteristic law and quantitative evaluation model for different cracks in contact line has important academic value and practical significance, and it has certain guiding significance for the detection technology of contact line in high-speed railway.

Abstract 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 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