Search

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]

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 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]

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]

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]

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]

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]

Artificial intelligence technology is receiving more and more attention in structural health monitoring. Fatigue crack detection in steel box girders in long-span bridges is an important and challenging task. This paper presents a semantic segmentation network model for this task based on DeepLabv3+, ResNet50, and active learning. Specifically, the classification network ResNet50 is re-tuned using the crack image dataset. Secondly, with the re-tuned ResNet50 as the backbone network, a crack semantic segmentation network was constructed based on DeepLabv3+, which was trained with the assistance of active learning. Finally, optimization for the probability threshold of the pixel category was performed to improve the pixel-level detection accuracy. Tests show that, compared with the crack detection network based on conventional ResNet50, this model can improve MIoU from 0.6181 to 0.7241. [ABSTRACT FROM AUTHOR]

As one of the early manifestations of road pavement structure degradation, road cracks will accelerate the deterioration of the road if not detected and repaired in time. Aiming at the problems of low recall and incomplete crack detection in current road detection, based on the U-Net network, this paper proposed an Attention-Dynamic Snake Convolution U-Net (ADSC-U-Net) network. Firstly, the dynamic snake-shaped convolution was added to the normal downsampling process to make the network adaptively focus on the slender and curved local features, which can solve the problem of low accuracy of small crack detection. Secondly, the attention mechanism was used to pay better attention to the significant features of positive samples under the condition of a large proportion gap between positive and negative samples, which solved the problem of the poor crack integrity detection effect. Finally, the dataset was expanded by random vertical and horizontal flip operations, which solved the problem of network training overfitting caused by the small-scale datasets. The experimental results showed that, when the input image had a resolution of 480 × 320, evaluation indices P, R, and F1 of ADSC-U-Net on the self-built dataset were 74.44%, 68.77%, and 69.42%, respectively. Compared to SegNet, DeepLab, and DeepCrack, the P was improved by 1.90%, 2.49%, and 11.64%, respectively; the R was improved by 8.01%, 4.70%, and 59.58%, respectively; and the comprehensive evaluation index F1 was improved by 5.73%, 4.02%, and 55.87%, respectively, which proves the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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

Stone curtain walls are widely used in contemporary architectures; however, their regular inspection is always labor-intensive, time-consuming, and hazardous due to the complex and enclosed spatial structure of these high-rise building enclosures. To address this issue, this study proposes an automated and novel inspection method, which is composed of the following three steps: First, we utilize 3D laser scanning technology to capture colored point cloud data of the stone curtain wall system; subsequently, by extracting and processing the integration of color and depth information, the stone panels and end sealants are precisely segmented; finally, various defects, such as cracks, unevenness, and irregularities, are automatically identified through artificial intelligence algorithms in a timely manner. To validate the proposed method, an on-site experiment was carried out to demonstrate the effectiveness in detecting multiple defects concurrently on stone curtain walls. The experimental results showed that our proposed method could provide a non-contact and automated inspection alternative for all the stone curtain walls with a high accuracy of anomaly detection, facilitating rational maintenance plans and strategies to ensure the safety and performance of these modern building enclosures. [ABSTRACT FROM AUTHOR]

Addressing the challenge of limited samples arising from the difficulty and high cost of pavement crack, image collecting and labeling, along with the inadequate ability of traditional data augmentation methods to enhance sample feature space, we propose VDCrackGAN, a generative adversarial network combining VAE and DCGAN, specifically tailored for pavement crack data augmentation. Furthermore, spectral normalization is incorporated to enhance the stability of network training, and the self-attention mechanism Swin Transformer is integrated into the network to further improve the quality of crack generation. Experimental outcomes reveal that in comparison to the baseline DCGAN, VDCrackGAN achieves notable improvements of 13.6% and 26.4% in the Inception Score (IS) and Fréchet Inception Distance (FID) metrics, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a method for detection of crack locations and the width of basin insulators is proposed. Based on the optical frequency domain reflectometry (OFDR) system, the system utilizes an FBG with high feedback for strain as well as temperature, which is affixed to the surface of the tub insulator, and a common single-mode optical fiber, which is used for transmitting data and connected to the optical backscattering reflectometry interrogator. The interrogator measures the backscattered light from the FBG, which varies with temperature or strain. The method has been used to measure the location and width of several different cracks and can locate the crack position with a spatial resolution of 1 mm and measure the crack width with a resolution of 0.77 mm. The method has been used to measure the position and width of insulators. This method provides a simple and fast approach to crack detection in insulators. [ABSTRACT FROM AUTHOR]

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

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

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

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]

Zhonghua Gate Castle is on the tentative list for Chinese World Cultural Heritage. Due to long-term sunshine, rain erosion, and man-made damage, its surface appears to have different degrees of cracks and other diseases. This paper centers on Zhonghua Gate Castle; terrestrial laser scanning is used to obtain the exterior wall point cloud data. A crack detection method based on point cloud data curved surface reconstruction is proposed. It involves data preprocessing, crack detection, and the analysis of crack features. This method initially uses data preprocessing techniques to improve data quality. These techniques include removing ground points and super-voxel segmentation. Subsequently, local surface reconstruction was employed to address the issue of missing point cloud data within cracks and the Euclidean clustering algorithm was used for precise crack identification. The article provides a detailed analysis of the geometric characteristics of cracks. They involve the calculation of length, width, and area. The results of the experiment demonstrate that the method could successfully identify cracks and extract geometric features and has millimeter-level accuracy compared to actual crack sizes. [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]

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

This paper proposes a road crack detection method based on residual and attention mechanisms to address the issues of difficult detection of small cracks on road surfaces in complex backgrounds and inaccurate crack detection edges. This method introduces residual modules in the encoder stage to better extract crack detail features and introduces attention mechanism modules in the skip connection structure of the network to better locate crack positions. Training and testing on public datasets have shown that compared with existing partial detection methods, our method has improved segmentation accuracy and generalization, and is more precise in segmenting small cracks, thus verifying the superiority of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

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

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

The crack detection method based on image processing has been a new achievement in the field of civil engineering inspection in recent years. Column piers are generally used in bridge structures. When a digital camera collects cracks on the pier surface, the loss of crack dimension information leads to errors in crack detection results. In this paper, an image stitching method based on Speed-Up Robust Features (SURFs) is adopted to stitch the surface crack images captured from different angles into a complete crack image to improve the accuracy of the crack detection method based on image processing in curved structures. Based on the proposed method, simulated crack tests of vertical, inclined, and transverse cracks on five different structural surfaces were conducted. The results showed that the influence of structural curvature on the measurement results of vertical cracks is very small and can be ignored. Nevertheless, the loss of depth information at both ends of curved cracks will lead to errors in crack measurement outcomes, and the factors that affect the precision of crack detection include the curvature of the surface and the length of the crack. Compared with inclined cracks, the structural curvature significantly influences the measurement results of transverse cracks, especially the length measurement results of transverse cracks. The image stitching method can effectively reduce the errors caused by the structural curved surface, and the stitching effect of three images is better than that of two images. [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]

Dam is an essential structure in hydraulic engineering, and its surface cracks pose significant threats to its integrity, impermeability, and durability. Automated crack detection methods based on computer vision offer substantial advantages over manual approaches with regard to efficiency, objectivity and precision. However, current methods face challenges such as misidentification, discontinuity, and loss of details when analyzing real-world dam crack images. These images often exhibit characteristics such as low contrast, complex backgrounds, and diverse crack morphologies. To address the above challenges, this paper presents a pure Vision Transformer (ViT)-based dam crack segmentation network (DCST-net). The DCST-net utilizes an improved Swin Transformer (SwinT) block as the fundamental block for enhancing the long-range dependencies within a SegNet-like encoder–decoder structure. Additionally, we employ a weighted attention block to facilitate side fusion between the symmetric pair of encoder and decoder in each stage to sharpen the edge of crack. To demonstrate the superior performance of our proposed method, six semantic segmentation models have been trained and tested on both a self-built dam crack dataset and two publicly available datasets. Comparison results indicate that our proposed model outperforms the mainstream methods in terms of visualization and most evaluation metrics, highlighting its potential for practical application in dam safety inspection and maintenance. [ABSTRACT FROM AUTHOR]

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

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

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

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

Cracks on concrete surfaces are vital factors affecting construction safety. Accurate and efficient crack detection can prevent safety-related accidents. Using drones to photograph cracks on a concrete surface and detect them through computer vision technology has the advantages of accurate target recognition, simple practical operation, and low cost. To solve this problem, an improved CenterNet concrete crack-detection model is proposed. Firstly, a channel-space attention mechanism is added to the original model to enhance the ability of the convolution neural network to pay attention to the image. Secondly, a feature selection module is introduced to scale the feature map in the downsampling stage to a uniform size and combine it in the channel dimension. In the upsampling stage, the feature selection module adaptively selects the combined features and fuses them with the output features of the upsampling. Finally, the target size loss is optimized from a Smooth L1 Loss to IoU Loss to lessen its inability to adapt to targets of different sizes. The experimental results show that the improved CenterNet model reduces the FPS by 123.7 Hz, increases the GPU memory by 62 MB, increases the FLOPs by 3.81 times per second, and increases the AP by 15.4% compared with the original model. The GPU memory occupancy remained stable during the training process and exhibited good real-time performance and robustness. [ABSTRACT FROM AUTHOR]

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

Regular crack inspection plays a significant role in the maintenance of concrete structures. However, most deep-learning-based methods suffer from the heavy workload of pixel-level labeling and the poor performance of crack segmentation with the presence of background interferences. To address these problems, the Deformable Oriented YOLOv4 (DO-YOLOv4) is first developed for crack detection based on the traditional YOLOv4, in which crack features can be effectively extracted by deformable convolutional layers, and the crack regions can be tightly enclosed by a series of oriented bounding boxes. Then, the proposed DO-YOLOv4 is further utilized in combination with the image processing techniques (IPTs), leading to a novel hybrid approach, termed DO-YOLOv4-IPTs, for crack segmentation. The experimental results show that, owing to the high precision of DO-YOLOv4 for crack detection under background noise, the present hybrid approach DO-YOLOv4-IPTs outperforms the widely used Convolutional Neural Network (CNN)-based crack segmentation methods with less labeling work and superior segmentation accuracy. [ABSTRACT FROM AUTHOR]

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

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

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

The maintenance level of rural roads is relatively low, and the automated detection of pavement distress is easily affected by the shadows of rows of trees, weeds, soil, and distress object scale disparities; this makes it difficult to accurately evaluate the distress conditions of the pavement. To solve the above problems, this study specifically designed a target detection network called Crack Convolution (CrackYOLO) for pavement crack extraction on rural roads. CrackYOLO is based on an improved YOLOv5. The shadow created by rows of trees leads to the loss of crack features in the feature extraction and downsampling stages of the network; therefore, CrackConv and Adapt-weight Down Sample (ADSample) were introduced to strengthen the ability to locate and identify cracks. Due to disturbances such as soil and weeds, which cause the extraction of more redundant features, the Channel And Spatial mixed attention mechanism (CAS) was introduced to enhance crack weight. To address the issue of missed detections of fine cracks due to significant scale variations in crack objects in the same image, Multi Scale Convolution (MSConv) and Multi Scale Head (MSHead) were incorporated during the feature fusion and prediction inference stages of the network, thereby improving the multi-scale detection performance. In order to verify the effectiveness of the proposed method, the detection accuracy of CrackYOLO when used on the LNTU_RDD_NC dataset was determined to be 9.99%, 12.79%, and 4.61% higher than that of the current pavement crack detection models YOLO-LWNet, Faster R-CNN, and YOLOv7. At the same time, we compare the above model on public datasets of different scenarios, and the experimental results show that CrackYOLO has the same strong performance in urban roads and other scenarios. [ABSTRACT FROM AUTHOR]

Pavement surface maintenance is pivotal for road safety. There exist a number of manual, time-consuming methods to examine pavement conditions and spot distresses. More recently, alternative pavement monitoring methods have been developed, which take advantage of unmanned aerial systems (UASs). However, existing UAS-based approaches make use of either image or LiDAR data, which do not allow for exploring the complementary characteristics of the two systems. This study explores the feasibility of fusing UAS-based imaging and low-cost LiDAR data to enhance pavement crack segmentation using a deep convolutional neural network (DCNN) model. Three datasets are collected using two different UASs at varying flight heights, and two types of pavement distress are investigated, namely cracks and sealed cracks. Four different imaging/LiDAR fusing combinations are created, namely RGB, RGB + intensity, RGB + elevation, and RGB + intensity + elevation. A modified U-net with residual blocks inspired by ResNet was adopted for enhanced pavement crack segmentation. Comparative analyses were conducted against state-of-the-art networks, namely U-net and FPHBN networks, demonstrating the superiority of the developed DCNN in terms of accuracy and generalizability. Using the RGB case of the first dataset, the obtained precision, recall, and F-measure are 77.48%, 87.66%, and 82.26%, respectively. The fusion of the geometric information from the elevation layer with RGB images led to a 2% increase in recall. Fusing the intensity layer with the RGB images yielded a reduction of approximately 2%, 8%, and 5% in the precision, recall, and F-measure. This is attributed to the low spatial resolution and high point cloud noise of the used LiDAR sensor. The second dataset crack samples obtained largely similar results to those of the first dataset. In the third dataset, capturing higher-resolution LiDAR data at a lower altitude led to improved recall, indicating finer crack detail detection. This fusion, however, led to a decrease in precision due to point cloud noise, which caused misclassifications. In contrast, for the sealed crack, the addition of LiDAR data improved the sealed crack segmentation by about 4% and 7% in the second and third datasets, respectively, compared to the RGB cases. [ABSTRACT FROM AUTHOR]

Crack inspection in railway sleepers is crucial for ensuring rail safety and avoiding deadly accidents. Traditional methods for detecting cracks on railway sleepers are very time-consuming and lack efficiency. Therefore, nowadays, researchers are paying attention to vision-based algorithms, especially Deep Learning algorithms. In this work, we adopted the U-net for the first time for detecting cracks on a railway sleeper and proposed a modified U-net architecture named Dense U-net for segmenting the cracks. In the Dense U-net structure, we established several short connections between the encoder and decoder blocks, which enabled the architecture to obtain better pixel information flow. Thus, the model extracted the necessary information in more detail to predict the cracks. We collected images from railway sleepers, processed them in a dataset, and finally trained the model with the images. The model achieved an overall F1-score, precision, Recall, and IoU of 86.5%, 88.53%, 84.63%, and 76.31%, respectively. We compared our suggested model with the original U-net, and the results demonstrate that our model performed better than the U-net in both quantitative and qualitative results. Moreover, we considered the necessity of crack severity analysis and measured a few parameters of the cracks. The engineers must know the severity of the cracks to have an idea about the most severe locations and take the necessary steps to repair the badly affected sleepers. [ABSTRACT FROM AUTHOR]

Detection of cracks in solar photovoltaic (PV) modules is crucial for optimal performance and long-term reliability. The development of convolutional neural networks (CNNs) has significantly improved crack detection, offering improved accuracy and efficiency over traditional methods. This paper presents a comprehensive review and comparative analysis of CNN-based approaches for crack detection in solar PV modules. The review discusses various CNN architectures, including custom-designed networks and pre-trained models, as well as data-augmentation techniques and ensemble learning methods. Additionally, challenges related to limited dataset sizes, generalizability across different solar panels, interpretability of CNN models, and real-time detection are discussed. The review also identifies opportunities for future research, such as the need for larger and more diverse datasets, model interpretability, and optimized computational speed. Overall, this paper serves as a valuable resource for researchers and practitioners interested in using CNNs for crack detection in solar PV modules. [ABSTRACT FROM AUTHOR]

Deep-learning- and unmanned aerial vehicle (UAV)-based methods facilitate structural crack detection for tall structures. However, contemporary datasets are generally established using images taken with handheld or vehicle-mounted cameras. Thus, these images might be different from those taken by UAVs in terms of resolution and lighting conditions. Considering the difficulty and complexity of establishing a crack image dataset, making full use of the current datasets can help reduce the shortage of UAV-based crack image datasets. Therefore, the performance evaluation of existing crack image datasets in training deep neural networks (DNNs) for crack detection in UAV images is essential. In this study, four DNNs were trained with different architectures based on a publicly available dataset and tested using a small UAV-based crack image dataset with 648 +pixel-wise annotated images. These DNNs were first tested using the four indices of precision, recall, mIoU, and F1, and image tests were also conducted for intuitive comparison. Moreover, a field experiment was carried out to verify the performance of the trained DNNs in detecting cracks from raw UAV structural images. The results indicate that the existing dataset can be useful to train DNNs for crack detection from UAV images; the TransUNet achieved the best performance in detecting all kinds of structural cracks. [ABSTRACT FROM AUTHOR]

Crack detection is an important task in bridge health monitoring, and related detection methods have gradually shifted from traditional manual methods to intelligent approaches with convolutional neural networks (CNNs) in recent years. Due to the opaque process of training and operating CNNs, if the learned features for identifying cracks in the network are not evaluated, it may lead to safety risks. In this study, to evaluate the recognition basis of different crack detection networks; several crack detection CNNs are trained using the same training conditions. Afterwards, several crack images are used to construct a dataset, which are used to interpret and analyze the trained networks and obtain the learned features for identifying cracks. Additionally, a crack identification performance criterion based on interpretability analysis is proposed. Finally, a training framework is introduced based on the issues reflected in the interpretability analysis. [ABSTRACT FROM AUTHOR]

Inductive thermography is a non-destructive testing method, whereby the workpiece to be inspected is slightly heated by a short inductive heating pulse. An infrared camera records the surface temperature during and after the heating pulse. As defects influence the induced eddy current distribution and the heat flow, they become highly visible in the evaluated infrared images. The deeper a crack is, the greater the obstacle it represents. In Inconel welded samples, short surface cracks (length 0.3–2 mm) were created using a so-called Varestraint test machine. The samples were inspected via inductive thermography and computer tomography (CT). Additional finite element simulations were calculated in order to model the thermography experiments. The comparison of the thermographic, CT and simulation results shows how the thermographic signal of a defect depends on its geometry. This information can be used for calibration to estimate the crack properties based on the thermographic inspection. [ABSTRACT FROM AUTHOR]

This preliminary study investigates the influence of test parameters on the detection and quantitative assessment of cracks using laser thermography. Cracks pose significant material design and analysis concerns, compromising the structural strength and durability of structures. Traditional crack detection methods have limitations, motivating the exploration of laser thermography. A finite element model (FEM) was developed and validated using finite thickness and surface cracks. Experimental tests were conducted, and the relative position between the laser spot and crack was investigated. The results showed the potential influence of the laser spot size and position on the crack detectability. This research contributes to advancing crack detection using non-destructive laser thermography techniques. [ABSTRACT FROM AUTHOR]