Crack Detection Method Research Articles

Guided wave-based crack detection in U-shaped flexural plate butt welds

Based on the guided wave, a crack detection method for U-shaped flexural plate (UFP) butt welds is established to realize damage localization and quantitative identification. Firstly, the semi-analytical finite element (SAFE) method was used to obtain the dispersion curve in a plate with weld by discretizing the cross-section and then solving the characteristic equation. Subsequently, numerical studies were performed to investigate the mode conversion and energy transfer efficiency in weld bending. Then the method was determined after the wave packet analysis of voltage responses under different damage conditions. Finally, a validation experiment was conducted in an actual UFP butt weld. The result indicates that there exist 10 guided wave modes in a plate with weld at 150 kHz, while only 2 modes in a flat plate; For curvature radius of weld bending substantially greater than the weld thickness, the guided wave energy mainly passes through the bending in the form of transmitted waves and the reflected wave formed in the bending can be ignored; The damage can be located with the defect echo of the specific guided wave and the error distance only accounting for 1.5% of the total weld length. It is effective to take wavelet energy as an indicator to characterize the damage degree. The method significantly improves crack detection efficiency and provides a scientific basis for monitoring similar structures.

DFP-Net: A Crack Segmentation Method Based on a Feature Pyramid Network

Timely detection of defects is essential for ensuring safe and stable operation of concrete buildings. Automatic segmentation of concrete buildings’ surfaces is challenging due to the high diversity of crack appearance, the detailed information, and the unbalanced proportion of crack pixels and background pixels. In this work, the Double Feature Pyramid Network is designed for high-precision crack segmentation. Our work reached the state-of-the-art level in crack segmentation, with key contributions outlined as follows: firstly, considering the diversity of crack shapes, the network constructs a feature pyramid containing three feature extraction backbones to extract the global feature map with three scale input images. In particular, due to the biggest challenge being too much single-pixel crack area, the targeted feature pyramid based on the high-resolution is added to extract adequate shallow semantic information. Lastly, designing a cascade feature fusion unit to aggregate the extracted multi-dimensional feature maps and obtain the final prediction. Compared with existing crack detection methods, the superior performance of this method has been verified based on extensive experiments, with Pixel Accuracy of 65.99%, Intersection over Union of 44.71%, and Recall of 62.95%, providing a reliable and efficient solution for the health monitoring and maintenance of concrete structures. This work contributes to the advancement of research and practical applications in related fields, offering robust support for the monitoring and maintenance of concrete structures.

An Automated Instance Segmentation Method for Crack Detection Integrated with CrackMover Data Augmentation.

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.

Landscape design of aging rehabilitation in community park based on improved YOLOv4

In the aging rehabilitation landscape design of community parks, the crack detection of landscape architecture is very important, which is related to the beauty and safety of landscape architecture. In order to improve the poor effect of current landscape building crack detection methods, an intelligent landscape building crack detection model based on improved YOLOv4 is proposed. Aiming at the inadequacy of information feature extraction in YOLOv4 algorithm, the coordinate attention module with extended convolution structure optimization is adopted to optimize it. Aiming at the defect that YOLOv4 algorithm has poor information feature utilization rate of shallow position, an improved K-means algorithm is designed to determine the optimal size of prior frame, so as to improve the performance of the algorithm. The performance of the algorithm is improved. Finally, combined with the above content, an intelligent detection model of landscape building cracks based on improved YOLOv4 is constructed. The results verify that the fitting degree of the model is 0.986, which is 0.008 and 0.016 higher than that of the CRF-CNN and Faster-RCNN models, respectively. The F1 value is 0.951, 0.10–0.12 higher than the two models. The accuracy was 98.26%, 0.47%-0.51% higher than the two models. Recall value was 96.44%, which was 0.47%-0.51% higher than that of the two models. The AUC value is 0.988, which is 0.009–0.016 higher than the two models. To sum up, the model built in the study can detect the cracks in landscape architecture with high accuracy and efficiency, and improve the safety and beauty of the landscape. Meanwhile, this model can assist in the design of aging rehabilitation landscape in community parks.

3D vision technologies for a self-developed structural external crack damage recognition robot

Persistent cracking and progressive damage can weaken the operational performance of structures such as bridges, dams, and concrete buildings. Consequently, research into automated, high-precision crack detection methods remains pivotal within the realm of structural health monitoring (SHM). Presently, scholars predominantly rely on two-dimensional (2D) image-based algorithms for crack detection. However, these methods commonly struggle to accurately locate the three-dimensional (3D) coordinates of cracks on large structures and to extract the 3D contours of cracks. To address this challenge, this study proposes an automated 3D crack detection system for structures based on high-precision Light Detection and Ranging (LiDAR) and camera fusion. Firstly, precise registration of images and LiDAR point clouds was achieved through accurate extrinsic calibration of the sensors. Secondly, the lightweight MobileNetV2_DeepLabV3 crack semantic segmentation network was employed to detect and locate cracks. Finally, by automatically guiding the robotic arm, an industry-standard depth camera was able to capture high-precision 3D information about the crack at close observation points. Compared with the existing studies, this study emphasizes the extraction of high-precision 3D crack features and verifies the validity of the method by comparing the measurement results with those of the traditional method, demonstrating a remarkable measurement accuracy reaching sub-millimeter levels (0.1 mm). Moreover, the study introduces a comprehensive hardware platform and algorithmic framework, offering pioneering theoretical methodologies and replicable equipment references for automated surveillance and monitoring systems dedicated to structural health.

Improved U-net network asphalt pavement crack detection method.

Road crack detection is one of the important parts of road safety detection. Aiming at the problems such as weak segmentation effect of basic U-Net on pavement crack, insufficient precision of crack contour segmentation, difficult to identify narrow crack and low segmentation accuracy, this paper proposes an improved U-net network pavement crack segmentation method. VGG16 and Up_Conv (Upsampling Convolution) modules are introduced as backbone network and feature enhancement network respectively, and the more abstract features in the image are extracted by using the Block depth separable convolution blocks, and the multi-scale features are captured and enhanced by higher level semantic information to improve the recognition accuracy of narrow cracks in the road surface. The improved network embedded the Ca(Channel Attention) attention mechanism in U-net's jump connection to enhance the crack portion to suppress background noise. At the same time, DG_Conv(Depthwise GSConv Convolution) module and UnetUp(Unet Upsampling) module are added in the decoding part to extract richer features through more convolutional layers in the network, so that the model pays more attention to the detailed part of the crack, so the segmentation accuracy can be improved. In order to verify the model's ability to detect cracks in complex backgrounds, experiments were carried out on CFD and Deepcrack datasets. The experimental results show that compared with the traditional U-net network F1-score and mIoU have increased by 13.6% and 9.9% respectively. Superior to advanced models such as U-net, Segnet and Linknet in accuracy and generalization ability, the improved model provides a new method for asphalt pavement crack detection. The model is more conducive to practical application and ground deployment, and can be applied in road maintenance projects.

A Real-Time Pavement Crack Detection Method Based on an Improved Lightweight Yolov8 Model

A Real-Time Pavement Crack Detection Method Based on an Improved Lightweight Yolov8 Model

A transformer-based deep learning method for automatic pixel-level crack detection and feature quantification

Purpose Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.Design/methodology/approach This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.Findings In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.Originality/value The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

Automated crack detection and a web tool using image processing techniques in concrete structures

Cracks indicates the real time deformity in concrete structures. It is characterized as discontinuity in terms of shape and size of the concrete structures. To ensure the structural health and safety, crack detection is an important task. The traditional methods of crack detection include visual introspection, ultrasonic and hand-held testing of crack. These methods require a high human intervention along with an experienced and skilled inspector. Moreover, these methods are subjective and time-consuming process which fails to identify the crack of the complex concrete structures properly. To overcome these issues, a grab-cut with improved Sobel has been proposed for automatic crack detection from the concrete structures. The proposed method works as a two-step model where cracks regions are segmented in the first step and a precise crack assessment is performed in the second step. Furthermore, to improve the efficacy of Sobel, the mask is modified with the aid of local variance of the image instead of using conventional mask of the filter. For the experimentation study, the images of self-prepared concrete sample have been acquired. The effectiveness of the proposed method has been compared with respect to various pre-existing methods like Sobel, Prewitt, Robert, LoG, Zero Cross and Canny. The comparative qualitative result exhibits that the proposed method surpasses the outcomes of the other pre-existing methods. Additionally, for easy implementation and application point of view a web tool of the proposed method has been developed. The web tool can be utilised by the civil infrastructure maintenance agency and construction engineers in the task of structure maintenance.

In-Depth Steel Crack Analysis Using Photoacoustic Imaging (PAI) with Machine Learning-Based Image Processing Techniques and Evaluating PAI-Based Internal Steel Crack Feasibility

Steel plays an indispensable role in our daily lives, permeating various products ranging from essential commodities and recreational gears to information technology devices and general household items. The meticulous evaluation of steel defects holds paramount importance to ensure the secure and dependable operation of the end products. Photoacoustic imaging (PAI) emerges as a promising modality for structural inspection in the realm of health monitoring applications. This study incorporates PAI experimentation to generate an image dataset and employs machine learning techniques to estimate the length and width of surface cracks. Furthermore, the research delves into the feasibility assessment of employing PAI to investigate internal cracks within a steel sample through a numerical simulation-based study. The study’s findings underscore the efficacy of the PAI in achieving precise surface crack detection, with an acceptable root mean square error (RMSE) of 0.63 ± 0.03. The simulation results undergo statistical analysis techniques, including the analysis of variance (ANOVA) test, to discern disparities between pristine samples and those featuring internal cracks at different locations. The results discern statistically significant distinctions in the simulated acoustic responses for samples with internal cracks of varying sizes at identical/different locations (p < 0.001). These results validate the capability of the proposed technique to differentiate between internal crack sizes and positions, establishing it as a viable method for internal crack detection in steel.

Multiple potential drop measurement – A new method for crack detection and crack length measurement

The Direct Current Potential Drop Method (DCPDM) is widely used for crack detection and crack length measurement in fatigue experiments. Unfortunately, crack length measurements are only possible when a calibration for a specified crack initiation site and potential probe location is available. Investigations by Hartweg and Bär (2019) as well as Nahbein and Bär (2022) on notched round specimens equipped with three potential probes placed in a distance of 120° on the circumference have shown that the crack initiation site can be determined using a simple vector-based model. In this model the normal vector of a plane span by the three potentials is calculated in cylindrical coordinates. In the present work a more detailed investigation of crack initiation and propagation is undertaken to show the possibilities offered by this method. During fatigue tests with the simultaneous measurement of the three potential drops using amplifiers of the control electronics, overloads were introduced in fixed intervals to mark the crack front on the fracture surface. The measurement of both crack depth and fractured area allows a calibration of the potential drop measurement. The calibration was performed on the radius coordinate n of the normal vector using a function suggested by Tiedemann (2016). The multiple potential drop measurement enables an early detection of crack initiation as well as a location of the crack initiation site on the circumference of the specimen. Using the Tiedemann function fitted to the measured data, it is possible to determine the crack length independent of the crack initiation site on the circumference of the specimen.

A three-stage pavement image crack detection framework with positive sample augmentation

Most pavement crack detection methods based on deep learning rely too much on pixel-wise labels, and are facing with sample imbalance problem. This paper proposes a three-stage pavement crack detection framework with positive sample augmentation by an autoencoder-deep convolutional generative adversarial network (E-DCGAN). Positive samples are the images labeled as crack in the training datasets. Firstly, crack regions are located using graph based on visual saliency (GBVS). Secondly, a convolutional neural network (CNN) model is designed to recognize crack pixels in the located crack regions. The E-DCGAN is explored to generate crack images, which are then added as augmented positive samples in the training dataset of the CNN model. The trained CNN model takes the located crack regions as inputs, outputs the recognized crack pixels. Finally, fine crack detection is realized through region growing. Crack contours are completely depicted pixel-wise based on the recognized crack pixels and the Breadth-first search. Experiments are carried out on two benchmark datasets and in practical applications. The results showed that the framework could detect cracks at the pixel level. Its performance is improved compared with standard fully convolutional network (FCN)-based and U-shaped CNN (U-Net)-based crack detection methods. In addition, the standard FCN-based and U-Net-based methods require accurate pixel-wise annotations, while the proposed framework only requires image-wise annotations. It can be concluded that the proposed framework not only effectively avoids the dependence on pixel-level annotations, improves the sample imbalance issues, but also achieves competitive detection performance over the FCN and U-Net based methods.

Embankment surface crack pixel-wise identification in UAV images based on a lightweight U-Network with transfer learning

Embankments in challenging environments experience cyclic external influences and loads that render their surfaces susceptible to a range of defects, with cracks particularly menacing. Therefore, accurate and efficient crack identification holds paramount significance for embankment safety. This paper addresses the challenges of low crack recognition rates on complex embankment surfaces and the high training costs associated with deep learning-based crack detection methods. We introduce a novel residual structure as the foundational innovation, followed by developing a streamlined U-shaped network (denoted as U²-Net_Aggregation) that amalgamates multi-scale insights by integrating skip connections. The newly devised U²-Net_Aggregation network demonstrates significant improvements in accuracy, intersection over union (IOU), and F1-score metrics through ablation tests on three distinct crack datasets. Comparisons with commonly employed crack detection methods (FCN, SegNet, U-Net, DeepCrack, and U²-Net) were conducted at the pixel level using an embankment crack dataset. Results unequivocally showcase enhancements in both accuracy and processing speed. To overcome the challenge of a high application threshold resulting from a scarcity of training data specific to embankment cracks, we compile an extensive dataset featuring diverse building crack scenarios, serving as a foundation for transfer learning. Following transfer learning, U2-Net_Aggregation attains recall, IOU, and F1-score values of 87.31%, 81.76%, and 90.06%, respectively. The proposed methodology enables swift and accurate crack detection by directly utilizing Unmanned Aerial Vehicle (UAV) images, offering a novel avenue for embankment crack detection in real-world field scenarios.

A systematic approach to pixel-level crack detection and localization with a feature fusion attention network and 3D reconstruction

Automated detection and localization of surface cracks by using deep learning and computer vision (CV) techniques are conducive to efficient structural condition assessment. However, insufficient feature resolution in deep architecture networks leads to misclassification and inaccurate segmentation of cracks. Furthermore, accurately localizing cracks within the overall structure is crucial for understanding the extent of damage to structural safety and durability. To address these challenges, this paper proposes a systematic approach for pixel-level surface crack detection and localization. The approach is based on a novel Feature Pyramid and Attention Feature Fusion Network (FPAFFN) and 3D reconstruction techniques. Two attention mechanisms named Attention Refinement Module (ARM) and Feature Fusion Module (FFM) are embedded in FPAFFN to strengthen the extraction and fusion of semantic and detailed information. With the cracks annotated images produced by FPAFFN, COLMAP and an open Multi-View Stereo are adapted to reconstruct the 3D model of the cracked structural components. Experimental studies on benchmark datasets CRACK500 and Cracktree200 and manually captured images validate the effectiveness of FPAFFN. The ARM and FFM embedded in FPAFFN enhance the feature learning efficiency and lead to a 3 % rise in the mean intersection ratio (mIoU). FPAFFN demonstrates remarkable generalization capability in detecting images with disturbances and outperforming other well-known crack detection methods in delineating crack edges and details. A subsequent experimental study on a damaged concrete slab shows that the proposed approach is capable of systematically detecting and localizing cracks. Therefore, it is promising to apply the proposed approach to practical structural inspection.

Optimized Wavelet and Wavelet Packet Transform Techniques for Assessing Crack Behavior in Curved Segments of Arched Beam Bridges Spanning Rivers

Our study focuses on the dynamic transient analysis of arched beam bridges over rivers, which face unique geohazards and challenges, including vibrations and dynamic loads that can affect structural integrity. The finite element software ANSYS v. 19.3 was employed to assess acceleration time histories at various bridge positions. Using MATLAB, we conducted wavelet packet decomposition to extract insights from the data, specifically isolating river-induced influences. In this article, the introduction of the wavelet packet rate index (WPERI) is presented as a novel metric for the detection of cracks in the curved bridge segments over rivers. The WPERI proves reliable in accounting for the river environment’s impact on structural integrity. Our findings highlight the sensitivity and precision of the WPERI in accurately detecting cracks and vulnerabilities in these riverine bridges. By combining WPT, finite element analysis, and signal decomposition, our research offers insights into tailored crack detection methods for riverine bridges. This study underscores the potential of WPT as a tool for identifying and characterizing cracks in curved bridge elements over rivers. The innovative WPERI provides a holistic approach to addressing structural issues, thus enhancing bridge durability amid changing environmental conditions. It contributes significantly to structural engineering and paves the way for the further exploration of river-specific crack detection techniques.

Photoacoustic speckle pattern interferometry for detecting cracks of different sizes.

A system and method for non-destructive detection of cracks of different width and depths based on digital speckle interferometry coupled with pulsed laser excitation is introduced and tested. Based on photoacoustic effect, acoustic waves are induced onto the rear of the samples by pumping a pulsed laser beam on it. The generated mechanical wave propagates from the rear surface of the sample to the front while front surface is monitored by speckle interferometry. In order to acquire information about surface deformation, the front surface is illuminated by continuous wave laser and interference are imaged onto the camera as speckle images. After processing the produced fringe patterns, it indicates the presence and location of the cracks in qualitative way. In this study, the system and method mentioned above are validated by detecting medium density fiberboard with simulated cracks. The fringe patterns from areas with or without defects are compared and discussed. Besides, the system and method to distinguish and predict cracks sizes is proposed and validated.

Crack Segmentation Extraction and Parameter Calculation of Asphalt Pavement Based on Image Processing.

Crack disease is one of the most serious and common diseases in road detection. Traditional manual methods for measuring crack detection can no longer meet the needs of road crack detection. In previous work, the authors proposed a crack detection method for asphalt pavements based on an improved YOLOv5s model, which is a better model for detecting various types of cracks in asphalt pavements. However, most of the current research on automatic pavement crack detection is still focused on crack identification and location stages, which contributes little to practical engineering applications. Based on the shortcomings of the above work, and in order to improve its contribution to practical engineering applications, this paper proposes a method for segmenting and analyzing asphalt pavement cracks and identifying parameters based on image processing. The first step is to extract the crack profile through image grayscale, histogram equalization, segmented linear transformation, median filtering, Sauvola binarization, and the connected domain threshold method. Then, the magnification between the pixel area and the actual area of the calibration object is calculated. The second step is to extract the skeleton from the crack profile images of asphalt pavement using the Zhang-Suen thinning algorithm, followed by removing the burrs of the crack skeleton image using the connected domain threshold method. The final step is to calculate physical parameters, such as the actual area, width, segments, and length of the crack with images obtained from the crack profile and skeleton. The results show that (1) the method of local thresholding and connected domain thresholding can completely filter noise regions under the premise of retaining detailed crack region information. (2) The Zhang-Suen iterative refinement algorithm is faster in extracting the crack skeleton of asphalt pavement, retaining the foreground features of the image better, while the connected-domain thresholding method is able to eliminate the missed isolated noise. (3) In comparison to the manual calibration method, the crack parameter calculation method proposed in this paper can better complete the calculation of crack length, width, and area within an allowable margin of error. On the basis of this research, a windowing system for asphalt pavement crack detection, WSPCD1.0, was developed. It integrates the research results from this paper, facilitating automated detection and parameter output for asphalt pavement cracks.

Detection Method of Cracks in Expressway Asphalt Pavement Based on Digital Image Processing Technology

Considering the limitations of the current pavement crack damage detection methods, this study proposes a method based on digital image processing technology for detecting highway asphalt pavement crack damage. Firstly, a non-subsampled contourlet transform is used to enhance the image of highway asphalt pavement. Secondly, the non-crack regions in the image are screened, and the crack extraction is completed by obtaining and enhancing the crack intensity map. Finally, the features of cracks are extracted and input into the support vector machine for classification and recognition to complete the detection of cracks in highway asphalt pavement. The experimental results show that the proposed method can effectively enhance the quality of a pavement image and precisely extract a crack area from the image with a high level of damage detection accuracy.

Tunnel Crack Detection Method and Crack Image Processing Algorithm Based on Improved Retinex and Deep Learning.

Tunnel cracks are the main factors that cause damage and collapse of tunnel structures. How to detect tunnel cracks efficiently and avoid safety accidents caused by tunnel cracks effectively is a research hotspot at present. In order to meet the need for efficient detection of tunnel cracks, the tunnel crack detection method based on improved Retinex and deep learning is proposed in this paper. The tunnel crack images collected by optical imaging equipment are used to improve the contrast information of tunnel crack images using the image enhancement algorithm, and this image enhancement algorithm has the function of multi-scale Retinex decomposition with improved central filtering. An improved VGG19 network model is constructed to achieve efficient segmentation of tunnel crack images through deep learning methods and then form the segmented binary image. The Zhang-Suen fast parallel-thinning method is used to obtain the skeleton map of the single-layer pixel, and the length and width information of the tunnel cracks are obtained. The feasibility and effectiveness of the proposed method are verified by experiments. Compared with other methods in the literature, the maximum deviation in the length of the tunnel crack is about 5 mm, and the maximum deviation in the width of the tunnel crack is about 0.8 mm. The experimental results show that the proposed method has a shorter detection time and higher detection accuracy. The research results of this paper can provide a strong basis for the health evaluation of tunnels.

Automated fatigue crack detection of orthotropic steel decks using an improved mask region-based convolutional neural network approach

Orthotropic steel decks (OSD) are widely used in the bridge industry, but they are susceptible to fatigue cracking due to various factors such as weld defects, overload traffic, and complicated welded joint configurations. Traditional crack detection methods rely mainly on visual inspection, which is time-consuming and prone to errors. Recently, many crack detection methods based on machine vision have been introduced. However, accurately and effectively detecting fatigue cracks of OSD remains challenging due to the indistinct nature of these cracks compared to the background and their susceptibility to various types of noise. To address these problems, this research proposes an improved Mask Region-based Convolutional Neural Network (Mask R-CNN). The proposed model enhances the feature extraction ability of the original Mask R-CNN by introducing a Convolutional Block Attention Module (CBAM) and Path Augmentation Feature Pyramid Network (PAFPN) into the backbone network. Additionally, a morphological closing operation is performed on the extracted fatigue cracks to address the problem of disjoint cracks. On the basis of crack mask closure, the pixel-wise quantization of cracks was achieved using relevant OpenCV functions. Evaluation on a crack dataset of OSD shows that the proposed model outperforms other methods with precision, recall, average precision (AP), and F1 score of 83.98%, 84.44%, 77.9%, and 84.21%, respectively. In conclusion, the experiment validates that the proposed model can effectively detect, segment, and quantify fatigue cracks of OSD.