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Crack detection and quantification play crucial roles in assessing the condition of concrete structures. Herein, a novel real-time crack detection and quantification method that leverages binocular vision and a lightweight deep learning model is proposed. In this methodology, the proposed method based on the following four modules is adopted: a lightweight classification algorithm, a high-precision segmentation algorithm, a semi-global block matching algorithm (SGBM), and a crack quantification technique. Based on the crack segmentation results, a framework is developed for quantitative analysis of the major geometric parameters, including crack length, crack width, and crack angle of orientation at the pixel level. Results indicate that, by incorporating channel attention and spatial attention mechanisms in the MBConv module, the detection accuracy of the improved EfficientNetV2 increased by 1.6% compared with the original EfficientNetV2. Results indicate that using the proposed quantification method can achieve low quantification errors of 2%, 4.5%, and 4% for the crack length, width, and angle of orientation, respectively. The proposed method can contribute to crack detection and quantification in practical use by being deployed on smart devices. [ABSTRACT FROM AUTHOR]

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]

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.)

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]

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]

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]

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]

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]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. 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.)

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]

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]

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.

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]

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]

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]

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]

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]

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]

Cracks pose a critical challenge in the preservation of historical buildings worldwide, particularly in fair-faced walls, where timely and accurate detection is essential to prevent further degradation. Traditional image processing methods have proven inadequate for effectively detecting building cracks. Despite global advancements in deep learning, crack detection under diverse environmental and lighting conditions remains a significant technical hurdle, as highlighted by recent international studies. To address this challenge, we propose an enhanced crack detection algorithm, CL-YOLOv8 (ConvNeXt V2-LSKA-YOLOv8). By integrating the well-established ConvNeXt V2 model as the backbone network into YOLOv8, the algorithm benefits from advanced feature extraction techniques, leading to a superior detection accuracy. This choice leverages ConvNeXt V2's recognized strengths, providing a robust foundation for improving the overall model performance. Additionally, by introducing the LSKA (Large Separable Kernel Attention) mechanism into the SPPF structure, the feature receptive field is enlarged and feature correlations are strengthened, further enhancing crack detection accuracy in diverse environments. This study also contributes to the field by significantly expanding the dataset for fair-faced wall crack detection, increasing its size sevenfold through data augmentation and the inclusion of additional data. Our experimental results demonstrate that CL-YOLOv8 outperforms mainstream algorithms such as Faster R-CNN, YOLOv5s, YOLOv7-tiny, SSD, and various YOLOv8n/s/m/l/x models. CL-YOLOv8 achieves an accuracy of 85.3%, a recall rate of 83.2%, and a mean average precision (mAP) of 83.7%. Compared to the YOLOv8n base model, CL-YOLOv8 shows improvements of 0.9%, 2.3%, and 3.9% in accuracy, recall rate, and mAP, respectively. These results underscore the effectiveness and superiority of CL-YOLOv8 in crack detection, positioning it as a valuable tool in the global effort to preserve architectural heritage. [ABSTRACT FROM AUTHOR]

The accurate segmentation of cracks in structural materials is crucial for assessing the safety and durability of infrastructure. Although conventional segmentation models based on deep learning techniques have shown impressive detection capabilities in these tasks, their performance can be restricted by small amounts of training data. Data augmentation techniques have been proposed to mitigate the data availability issue; however, these systems often have limitations in texture diversity, scalability over multiple physical structures, and the need for manual annotation. In this paper, a novel generative artificial intelligence (GAI)-driven data augmentation framework is proposed to overcome these limitations by integrating a projected generative adversarial network (ProjectedGAN) and a multi-crack texture transfer generative adversarial network (MCT2GAN). Additionally, a novel metric is proposed to evaluate the quality of the generated data. The proposed method is evaluated using three datasets: the bridge crack library (BCL), DeepCrack, and Volker. From the simulation results, it is confirmed that the segmentation performance can be improved by the proposed method in terms of intersection over union (IoU) and Dice scores across three datasets. [ABSTRACT FROM AUTHOR]

Disaster-resilient dams require accurate crack detection, but machine learning methods cannot capture dam structural reaction temporal patterns and dependencies. This research uses deep learning, convolutional neural networks, and transfer learning to improve dam crack detection. Twelve deep-learning models are trained on 192 crack images. This research aims to provide up-to-date detecting techniques to solve dam crack problems. The finding shows that the EfficientNetB0 model performed better than others in classifying borehole concrete crack surface tiles and normal (undamaged) surface tiles with 91% accuracy. The study's pre-trained designs help to identify and to determine the specific locations of cracks. [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]

Asphalt cracks are the initial indication and major pavement structural distress in the field of civil engineering because it might potentially intimidate road traffic and highway safety. Managing and maintaining pavement structures are the two crucial issues faced eventually by road administrators. It is essential to take immediate action regarding pavement structural damages to prevent its severity. In recent times, there established numerous deep learning-based image processing methods to detect pavement cracks, but due to the presence of interference factors such as noises and road markings in the images, those methods failed to provide high accuracy and efficiency. Therefore, with the aim to yield high accuracy of crack detection, this article designs a novel asphalt pavement crack identification system "GoogleNet transfer learning with improved gorilla optimized kernel extreme learning machine" (GNet TL with IGT-KELM). The road crack images used for perusal are acquired from NHA12D dataset, in which it consists of 40 asphalt pavement images with two different viewpoints. For the purpose of data balancing, some non-crack images are gathered by field survey. The images with large noise distortions and unwanted background information influence crack detection performance so that the preprocessing steps are executed before applying it to the prediction system. The most significant crack and non-crack features from the images are extracted using GNet TL model. With the extracted features, the KELM is well-trained to detect cracks and non-cracks separately. To increase crack detection performance of the classifier, an improved gorilla troop optimizer is introduced to optimize the KELM parameters. The experimental finding reveals that the proposed detection mechanism achieves crack recognition accuracy of about 98.93%, which is considerably greater than compared baseline approaches. [ABSTRACT FROM AUTHOR]

Copyright of Dicle University Journal of Engineering / Dicle Üniversitesi Mühendislik Dergisi is the property of Dicle Universitesi 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.)

Abstract U-net, a fully convolutional network-based image segmentation method, has demonstrated widespread adaptability in the crack segmentation task. The combination of the semantically dissimilar features of the encoder (shallow layers) and the decoder (deep layers) in the skip connections leads to blurry features map and leads to undesirable over- or under-segmentation of target regions. Additionally, the shallow architecture of the U-Net model prevents the extraction of more discriminatory information from input images. This paper proposes a Residual Sharp U-Net (RS-Net) architecture for crack segmentation and severity assessment in pavement surfaces to address these limitations. The proposed architecture uses residual block in the U-Net model to extract a more insightful representation of features. In addition to that, a sharpening kernel filter is used instead of plain skip connections to generate a fine-tuned encoder features map before combining it with decoder features maps to reduce the dissimilarity between them and smoothes artifacts in the network layers during early training. The proposed architecture is also integrated with various morphological operations to assess the severity of cracks and categorize them into hairline, medium, and severe labels. Experiments results demonstrated that the RS-Net model has promising segmentation performance, outperforming earlier U-Net variations on testing data for crack segmentation and severity assessment, with a promising accuracy (>0.97)

Buildings with cracks are extremely hazardous because they have the potential to cause destruction. Numerous occupants of structures such as houses and buildings are at risk when cracks appear. There are numerous techniques for identifying fractures in structures, including visual inspection, tool use, and expert inspection. The present study employed computer vision, a form of artificial intelligence, to detect cracks in buildings. The main objective of this research is to construct a prototype capable of real-time monitoring of cracks in building walls. This research makes use of a methodology that combines machine learning and deep learning. Machine learning is employed in the classification process, whereas deep learning is utilized for the extraction of features. This research employs MobileNetV2 as its deep learning architecture and K-NN, Naive Bayes, SVM, XGBoost, and Random Forest as its machine learning classifiers. Test results show that when dividing the 80:20 dataset, XGBoost algorithms can produce the highest accuracy, sensitivity, and specificity values of 99%. Tests in the real environment are performed by deploying Raspberry Pi. Test results show that the prototype can detect cracks inthe structure surfaceat a distance of 10 meters in a bright environment. The crack detection process is carried out in real time at an average speed of 42fps.

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]

U-net, a fully convolutional network-based image segmentation method, has demonstrated widespread adaptability in the crack segmentation task. The combination of the semantically dissimilar features of the encoder (shallow layers) and the decoder (deep layers) in the skip connections leads to blurry features map and leads to undesirable over- or under-segmentation of target regions. Additionally, the shallow architecture of the U-Net model prevents the extraction of more discriminatory information from input images. This paper proposes a Residual Sharp U-Net (RS-Net) architecture for crack segmentation and severity assessment in pavement surfaces to address these limitations. The proposed architecture uses residual block in the U-Net model to extract a more insightful representation of features. In addition to that, a sharpening kernel filter is used instead of plain skip connections to generate a fine-tuned encoder features map before combining it with decoder features maps to reduce the dissimilarity between them and smoothes artifacts in the network layers during early training. The proposed architecture is also integrated with various morphological operations to assess the severity of cracks and categorize them into hairline, medium, and severe labels. Experiments results demonstrated that the RS-Net model has promising segmentation performance, outperforming earlier U-Net variations on testing data for crack segmentation and severity assessment, with a promising accuracy (>0.97) [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]

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]

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]

Crack detection is significant to building repair and maintenance; however, conventional inspection is a labor-intensive and time-consuming process for field engineers. This paper proposes a three-step computer vision-based framework to quickly recognize concrete cracks and automatically identify their length, maximum width, and area in damage images. In step one, a region-based convolutional neural network (YOLOv8) is applied to train the crack localizing model. In step two, Gaussian filtering, Canny, and FindContours are integrated to extract the reference contour (a pre-designed seal) to obtain the conversion scale between pixels and millimeter-wise sizes. In step three, the recognized crack bounding box is cropped, and the ApproxPolyDP function and Hough transform are performed to quantify crack dimensions based on the conversion ratio. The developed framework was validated on a dataset of 4630 crack images, and the model training took 150 epochs. Results show that the average crack detection accuracy reaches 95.7%, and the precision of quantified dimensions is over 90%, while the error increases as the crack size grows smaller (increasing to 8% when the crack width is within 1 mm). The proposed method can help engineers to efficiently achieve crack information at building inspection sites, while the reference frame must be pre-marked near the crack, which may limit the scope of application scenarios. In addition, the robustness and accuracy of the developed image processing techniques-based crack quantification algorithm need to be further improved to meet the requirements in real cases when the crack is located within a complex background. [ABSTRACT FROM AUTHOR]

In recent decades, wood structures have gained significant attention for their ecological benefits and architectural versatility. The performance of wood, a popular construction material, often depends on the integrity of its connections. This study focuses on bolted glulam timber connections, which are strong but prone to cracks that pose structural health challenges. Traditional crack evaluation methods are manual, time-consuming, and error-prone. To address these issues, this research proposes a two-stage performance evaluation method. In the first stage, an innovative approach called 'Enhanced Deep Line Segment Detection' (Deep LSD), a non-supervised machine learning technique, is used for crack detection without relying on large, annotated datasets, thus enhancing efficiency and adaptability. In the second stage, cyclic loading assays simulate varying damage stages to collect data and establish a correlation between crack states and connection damage. The Park and Ang damage model is employed within this framework to assess the extent of damage. The efficacy of enhanced deep LSD is confirmed by comparing detected crack areas with ground truth measurements, yielding a high R-squared value of 0.98 and a minimal error margin of 1.41. Additionally, a damage index based on the Chinese standard (GB/T 24335-2009) is used to classify damage across different connection groups, ensuring robustness and alignment with established practices. [ABSTRACT FROM AUTHOR]

To solve the problem of incomplete and inaccurate pavement crack detection, an improved U-Net model based on dual attention mechanism and multi-feature fusion is proposed. Firstly, a new encoding module ACI is designed, which has the feature of multi-scale feature extraction, significantly improves the sensing ability of the damaged area, reduces the background interference, and realizes more accurate segmentation. Secondly, a new decoding module HAD is designed, which avoids the network degradation problem caused by gradient vanishing and the growth of network layers and can retain the most subtle feature information during the decoding process. Finally, convolutional block attention module (CBAM) is introduced in the encoding part to effectively extract global and local detail information, and the criss-cross attention mechanism is also introduced in the decoding part to prevent the loss of marginalized information. The model proposed in this article was tested on the public datasets DeepCrack, CrackSeg478, and AsphaltCrack300, and compared with other advanced methods. The experimental results indicate that this method can detect road cracks more accurately and possesses considerable robustness. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) is increasingly being used in the field of bridge engineering, and the technology for monitoring bridges has undergone a radical change. It has evolved from the initial local monitoring and assessment, which relied mainly on manual work, to the current all-round and full-time intelligent assessment provided by intelligent monitoring systems. This paper reviews the development of SHM technology in the civil engineering field and examines two current artificial intelligence (AI) methods in bridge SHM, namely knowledge-driven and data-driven approaches. The advantages and disadvantages of these two AI methods are analyzed, and future development trends are also discussed. The overview results reveal that knowledge-driven methods have the advantages of interpretability and stability. However, their current application is limited, and significant technical bottlenecks remain. On the other hand, the data-driven approach demonstrates higher efficiency and accuracy. Nevertheless, it is characterized by instability and insecurity due to its "black-box" nature, which hinders its ability to explain the internal operation mechanism. Given these findings, the hybrid knowledge-data-driven approach emerges as a potential solution. This approach can effectively integrate the advantages of both knowledge-driven and data-driven methods while avoiding their respective disadvantages. Consequently, the hybrid approach proves to be more stable, safe, and efficient in practical applications. [ABSTRACT FROM AUTHOR]

Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning, with convolutional neural networks (CNN) playing an important role in this field. However, as the performance of crack detection in cement pavement improves, the depth and width of the network structure are significantly increased, which necessitates more computing power and storage space. This limitation hampers the practical implementation of crack detection models on various platforms, particularly portable devices like small mobile devices. To solve these problems, we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature fusion. Firstly, we use small channel convolution to construct shallow feature extractionmodule (SFEM) to extract low-level feature information of cracks in cement pavement images, in order to obtainmore information about cracks in the shallowfeatures of images. In addition,we construct large kernel atrous convolution (LKAC) to enhance crack information, which incorporates coordination attention mechanism for non-crack information filtering, and large kernel atrous convolution with different cores, using different receptive fields to extract more detailed edge and context information. Finally, the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module, and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction map. We evaluate our method on three public crack datasets: DeepCrack, CFD, and Crack500. Experimental results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods, which achieves Precision (P) 87.2%, Recall (R) 87.7%, and F-score (F1) 87.4%. Thanks to our lightweight crack detectionmodel, the parameter count of the model in real-world detection scenarios has been significantly reduced to less than 2M. This advancement also facilitates technical support for portable scene detection. [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]

As the backbone of transportation networks, the structural integrity of bridges is paramount for ensuring public safety and the efficient flow of goods and people. The traditional method that is Manual inspection methods for crack detection are labor intensive and often subjected to human error. This research explores an innovative approach to address this challenge by leveraging Convolutional Neural Networks for automated crack identification in bridge infrastructure. The proposed model employs a dataset encompassing concrete bridge conditions, and crack manifestations to train and evaluate the selected model. The accuracy of the prediction was verified by the test sets. The introduced model demonstrated a crack detection accuracy of 99% without relying on pre-training. Experimental results indicated that, when compared to existing classification models, the suggested model exhibited superior performance. Notably, the proposed model surpassed the ResNet50 model in terms of effectiveness. [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]

Crack detection is crucial to providing information for assessing pavement condition and maintaining the safety of infrastructure. Traditional manual crack detection methods are relatively time-consuming and thus have gradually been replaced by automatic detection in recent years. However, most semantic segmentation algorithms require the manual labelling of a large amount of data, which consumes a great deal of time. A semi-supervised semantic word segmentation method based on cross-consistency training for road crack detection is established in this paper. To take advantage of unlabelled crack samples, this study enforced consistency between the primary and secondary decoder predictions, using different disturbed versions of the encoder output as inputs to improve the encoder representation. When only 60% of the annotated data were used, the applied method achieved a better performance than other mainstream semantic segmentation algorithms. Therefore, the applied method can be used in automated and efficient pavement detection for pavement inspection and maintenance. [ABSTRACT FROM AUTHOR]

Detecting cracks is vital for inspecting and maintaining concrete structures, enabling early intervention and preventing potential damage. The advent of computer vision and image processing in civil engineering has ushered in deep learning-based semi-automatic/automatic techniques, replacing traditional visual inspections. These methods, driven by autonomous diagnosis, have applications across various sectors, fostering rapid progress in civil engineering. In this study, we present an approach that combines vision transformers and convolutional neural networks (CNN) for autonomously diagnosing cracks in bridges, roads, and walls. Performance enhancement was achieved through transfer learning, data augmentation, and optimized hyperparameters, utilizing popular CNN and vision transformers (ViT) architectures. The proposed method was tested on the SDNET2018 dataset, comprising over 56,000 images. Experimental results demonstrated the approach's effectiveness, achieving high accuracy in detecting road cracks at 96.41%, wall cracks at 92.76%, and bridge cracks at 92.81%. These findings highlight the promising potential of deep learning in this field. [ABSTRACT FROM AUTHOR]

Characterizing defects in 2D materials, such as cracks in chemical vapor deposited (CVD)-grown hexagonal boron nitride (hBN), is essential for evaluating material quality and reliability. Traditional characterization methods are often time-consuming and subjective and can be hindered by the limited optical contrast of hBN. To address this, we utilized a YOLOv8n deep learning model for automated crack detection in transferred CVD-grown hBN films, using MATLAB's Image Labeler and Supervisely for meticulous annotation and training. The model demonstrates promising crack-detection capabilities, accurately identifying cracks of varying sizes and complexities, with loss curve analysis revealing progressive learning. However, a trade-off between precision and recall highlights the need for further refinement, particularly in distinguishing fine cracks from multilayer hBN regions. This study demonstrates the potential of ML-based approaches to streamline 2D material characterization and accelerate their integration into advanced devices. [ABSTRACT FROM AUTHOR]

Abstract The railway rivet is one of the most important and easily damaged parts of the connection. If rivets develop cracks during the production process, their load-bearing capacity will be reduced, thereby increasing the risk of failure. Fluorescent magnetic particle flaw detection (FMPFD) is a widely used inspection method for train fasteners. Manual inspection is not only time-consuming but also prone to miss detection, therefore intelligent detection system has important application value. However, the fluorescent crack images obtained by FMPFD present challenges for intelligent detection, such as the dense, multi-scaled and uninstantiated cracks. In addition, there is limited research on fluorescent rivet crack detection. This paper adopts instance segmentation to achieve automatic cracks detection of rivets. A decentralized target center and low overlap rate labeling method is proposed, and a Gaussian-weighted correction post-processing method is introduced to improve the recall rate in the areas of dense cracks. An efficient channel spatial attention mechanism for feature extraction is proposed in order to enhance the detection of multi-scale cracks. For uninstantiated cracks, an improvement of crack detection in uninstantiated regions based on multi task feature learning is proposed, thoroughly utilizing the semantic and spatial features of the fluorescent cracks. The experimental results show that the improved methods are better than the baseline and some cutting-edge algorithms, achieving a recall rate and mAP0.5 of 86.4% and 90.3%. In addition, a single coil non-contact train rivet composite magnetization device is built for rivets that can magnetize different shapes of rivets and has universality.