Crack Detection Method Research Articles

Validation of pneumatic crack detection in hole expansion test

According to the standard ISO 16630, the hole expansion test (HET) has to be stopped at the instant when the first through-thickness crack appears at the edge of the hole. The detection of the crack is challenging, since the maximum opening width should not exceed 0.1 mm at the inner diameter according to the newest version of the standard from 2017. A pneumatic crack detection has been introduced in which a pressure drop is used to identify the through-thickness crack. In the present paper, this method for stopping the hole expansion test is validated. It was tested for steels with different edge crack sensitivities and compared to stopping with force drop method. Therefore, the curves of pressure and force have been analysed. The influence of hole insertion method (shear cutting or abrasive water jet cutting) as well as specific hole expansion parameters (punch speed, air pressure) have been investigated. Besides the expanded diameters to evaluate the edge crack sensitivity, the opening width of the edge cracks were analysed to determine the suitability of the crack detection methods for different hole expansion ratios (HER).

Autonomous surface crack identification for concrete structures based on the you only look once version 5 algorithm

Failure to repair roads in a timely manner may shorten their life and even cause traffic accidents. Thus, accurate crack detection and reasonable classification are crucial for road safety evaluation. In this study, an improved network model based on the You Only Look Once version 5 algorithm is presented, with three additional modules: The first module improves the data processing speed by replacing the C3 module in the original network with a lightweight network model. The second module was used to lighten network weight by reusing a simple convolution structure to equivalently represent the calculation of a convolution layer as a weighted sum of several small convolution blocks. And the third module is used to improve the detection accuracy by removing the upsampling and performing three-way splicing. The proposed model can detect different types of cracks, and an extensive ablation study is reported based on various combinations of the proposed modules. Based on training on a database of 5484 images, the results show that the improved network proposed in this study can effectively identify pavement cracks. Compared with the original network, mean Average Precision is increased by 5.98%, the inference time is reduced by 4.82%, and the model weight is decreased by 17.36%. Additionally, to comply with engineering practice, comparative experiments were conducted on the pre-rotated dataset. The results showed that compared with You Only Look Once version 8, the improved algorithm improved accuracy, recall, average accuracy, and F1 score by 3.28%, 8.46%, 3.79% and 5.89%, respectively. This study can serve as an important reference for the development of crack detection methods.

Enhancing Concrete Crack Image Detection Using MobileNetV2 and Transfer Learning

The safety of civil engineering structures such as bridges and buildings is paramount, necessitating the early detection and repair of cracks to prevent structural failures. Traditional crack detection methods, including manual inspections and basic image processing, are often hampered by slow detection speeds and limited accuracy. This study explores the integration of advanced deep learning techniques, specifically MobileNetV2, enhanced through transfer learning, to improve the efficiency and accuracy of crack detection. The "Concrete Crack Images for Classification" dataset was employed, featuring 40,000 images from real-world scenarios. Our approach leverages the lightweight architecture of MobileNetV2 and the efficiency of transfer learning to create a robust model that not only reduces computational demands but also achieves high accuracy, with a validation accuracy rate of 99.38%. This paper details the model training, the innovative use of MobileNetV2, and comparative analyses with other deep learning models, demonstrating significant improvements over traditional methods. The findings underscore the potential of MobileNetV2 and transfer learning in practical applications, offering a promising avenue for future research in automated crack detection across various engineering fields.

Underwater dam crack image generation based on unsupervised image-to-image translation

Underwater crack detection is necessary for the safe operation of concrete dams. Current deep-learning-based underwater crack detection methods rely heavily on a large number of crack images; these images are difficult to collect because of complex and dangerous underwater environments. This study proposes a method that can generate underwater dam crack images using above-water dam crack images through an image-to-image translation method. CycleGAN, which is more powerful than other models, was optimized to generate underwater dam crack images by translating existing above-water dam crack images into underwater styles. The generated underwater images were validated using the following major computer vision tasks: classification, object detection, and semantic segmentation. The results demonstrate the potential of these generated images for training and optimizing deep-learning-based crack-detection methods. This method is expected to solve the problem of insufficient data and enhance deep-learning-based detection networks.

Concrete Highway Crack Detection Based on Visible Light and Infrared Silicate Spectrum Image Fusion.

Cracks provide the earliest and most immediate visual response to structural deterioration of asphalt pavements. Most of the current methods for crack detection are based on visible light sensors and convolutional neural networks. However, such an approach obviously limits the detection to daytime and good lighting conditions. Therefore, this paper proposes a crack detection technique cross-modal feature alignment of YOLOV5 based on visible and infrared images. The infrared spectrum characteristics of silicate concrete can be an important supplement. The adaptive illumination-aware weight generation module is introduced to compute illumination probability to guide the training of the fusion network. In order to alleviate the problem of weak alignment of the multi-scale feature map, the FA-BIFPN feature pyramid module is proposed. The parallel structure of a dual backbone network takes 40% less time to train than a single backbone network. As determined through validation on FLIR, LLVIP, and VEDAI bimodal datasets, the fused images have more stable performance compared to the visible images. In addition, the detector proposed in this paper surpasses the current advanced YOLOV5 unimodal detector and CFT cross-modal fusion module. In the publicly available bimodal road crack dataset, our method is able to detect cracks of 5 pixels with 98.3% accuracy under weak illumination.

Crack detection based on attention mechanism with YOLOv5

AbstractIn order to reduce the manual workload and reduce the maintenance cost, it is particularly important to realize automatic detection of cracks. Aiming at the problems of poor real‐time performance and low precision of traditional pavement crack detection, a crack detection method based on improved YOLOv5 one‐step target detection algorithm of convolutional neural network is proposed by using the advantages of depth learning network in target detection. The images were manually marked with LabelImg annotation software, and then the network model parameters were obtained through improving the YOLOv5 network training. Finally, the cracks are verified and detected by the established model. In addition, the precision and speed of crack detection using YOLOv3, YOLOv5s, and YOLOv5s‐attention models are compared by using Precision, Recall, and F1. After comparison, it is found that the detection precision of YOLOv5s‐attention is improved by 1.0%, F1 by 0.9%, and mAP@.5 by 1.8%.

Deep learning-based three-dimensional crack damage detection method using point clouds without color information

Automated high-precision crack detection on building structures under poor lighting conditions poses a significant challenge for traditional image-based methods. Overcoming this challenge is crucial to enhance the practical applicability of structural health monitoring and rapid damage assessment, especially in post-disaster scenarios like earthquakes. To address this challenge, this paper presents a deep learning-based three-dimensional crack detection method that utilizes light detection and ranging (LiDAR) point cloud data. Our method is specifically designed to address crack detection without relying on color information input, resulting in high-precision and robust apparent damage detection. The key contribution of this paper is the NL-3DCrack model, which enables automated three-dimensional crack semantic segmentation. This model comprises a feature embedding module, an incomplete neighbor feature extraction module, a decoder, and morphological filtering. Notably, we introduce an innovative incomplete neighbor mechanism to effectively mitigate the impact of outliers. To validate the effectiveness of our proposed method, we establish two three-dimensional crack detection datasets, namely the Luding dataset and the terrestrial laser scanner dataset, which are based on earthquake disasters. Experimental results demonstrate that our method achieves remarkable performance, with an intersection-over-union of 39.62% and 51.33% on the respective test sets, surpassing existing point cloud-based semantic segmentation models. Ablation experiments further confirm the effectiveness of our approach. In summary, our method showcases exceptional crack detection performance on LiDAR data using only XYZI channels. With its high precision and reliable results, it offers significant utility in real-world applications, contributing to improved structural health monitoring and rapid damage assessment after disasters, particularly in post-earthquake scenarios.

A shallow 2D-CNN network for crack detection in concrete structures

PurposeThis study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.Design/methodology/approachThis paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.FindingsResults show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.Originality/valueThis paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.

A Concise State-of-the-Art Review of Crack Monitoring Enabled by RFID Technology

Cracking is an important factor affecting the performance and life of large structures. In order to maximize personal safety and reduce costs, it is highly necessary to carry out research on crack monitoring technology. Sensors based on Radio Frequency Identification (RFID) antennas have the advantages of wireless and low cost, which makes them highly competitive in the field of structure health monitoring (SHM). Thus, this study systematically summarizes the research progress of crack monitoring based on RFID technology in recent years. Firstly, this study introduces the causes of cracks and the traditional monitoring methods. Further, this study summarizes several main RFID-based crack monitoring and detection methods, including crack monitoring based on chipless RFID technology, passive RFID technology, and ultra-high-frequency (UHF) RFID technology, including the implementation methods, as well as the advantages and disadvantages of those technologies. In addition, for RFID-based crack monitoring applications, the two most commonly used materials are concrete materials and metal materials, which are also illustrated in detail. In general, this study can provide technical support and a theoretical basis for crack monitoring and detection to ensure the safety of engineering structures.

An efficient approach for automatic crack detection using deep learning

PurposeAutomation of detecting cracked surfaces on buildings or in any industrially manufactured products is emerging nowadays. Detection of the cracked surface is a challenging task for inspectors. Image-based automatic inspection of cracks can be very effective when compared to human eye inspection. With the advancement in deep learning techniques, by utilizing these methods the authors can create automation of work in a particular sector of various industries.Design/methodology/approachIn this study, an upgraded convolutional neural network-based crack detection method has been proposed. The dataset consists of 3,886 images which include cracked and non-cracked images. Further, these data have been split into training and validation data. To inspect the cracks more accurately, data augmentation was performed on the dataset, and regularization techniques have been utilized to reduce the overfitting problems. In this work, VGG19, Xception and Inception V3, along with Resnet50 V2 CNN architectures to train the data.FindingsA comparison between the trained models has been performed and from the obtained results, Xception performs better than other algorithms with 99.54% test accuracy. The results show detecting cracked regions and firm non-cracked regions is very efficient by the Xception algorithm.Originality/valueThe proposed method can be way better back to an automatic inspection of cracks in buildings with different design patterns such as decorated historical monuments.

GBC-BCD: an improved bridge crack detection method based on bidirectional Laplacian pyramid structure with lightweight attention mechanism convolution

ABSTRACT As roads continue to expand globally, the construction of a larger number of bridges has significantly increased the burden on bridge maintenance. Traditional methods for bridge crack detection often suffer from low efficiency and accuracy. To address these issues, we propose an efficient one-stage bridge crack detection method called GBC-BCD based on the YOLOv8n framework. To improve the model’s performance, we introduce the Coordinated Attention mechanism to enhance spatial context awareness and the Bidirectional Feature Pyramid Network to strengthen the ability of feature fusion and multi-scale feature learning. At the same time, in order to make the model more efficient while improving its performance, we introduce the Ghost Module, which significantly reduces the size of the model and raises the speed of object detection. Moreover, we employ transfer learning to improve training efficiency and conserve computational resources for small datasets. Experiments show that our model has the characteristics of lightweight and high speed. Compared with famous lightweight object detection algorithms, the model size is at most reduced by 86.6%, and realises real-time (625 FPS) processing of images ( 640 × 640 ), an 8.3 times increment in speed.

A new global multiplexing structure of original features for road crack detection

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

An Intelligent Detection and Classification Model Based on Computer Vision for Pavement Cracks in Complicated Scenarios

With the extension of road service life, cracks are the most significant type of pavement distress. To monitor road conditions and avoid excessive damage, pavement crack detection is absolutely necessary and an indispensable part of road periodic maintenance and performance assessment. The development and application of computer vision have provided modern methods for crack detection, which are low in cost, less labor-intensive, continuous, and timely. In this paper, an intelligent model based on a target detection algorithm in computer vision was proposed to accurately detect and classify four classes of cracks. Firstly, by vehicle-mounted camera capture, a dataset of pavement cracks with complicated backgrounds that are the most similar to actual scenarios was built, containing 4007 images and 7882 crack samples. Secondly, the YOLOv5 framework was improved from the four aspects of the detection layer, anchor box, neck structure, and cross-layer connection, and thereby the network’s feature extraction capability and small-sized-target detection performance were enhanced. Finally, the experimental results indicated that the proposed model attained an AP of the four classes of 81.75%, 83.81%, 98.20%, and 92.83%, respectively, and a mAP of 89.15%. In addition, the proposed model achieved a 2.20% missed detection rate, representing a 6.75% decrease over the original YOLOv5. These results demonstrated the effectiveness and practicality of our proposed model in addressing the issues of low accuracy and missed detection for small targets in the original network. Overall, the implementation of computer vision-based models in crack detection can promote the intellectualization of road maintenance.

Ultrasonic penetration method for weld root crack in orthotropic steel decks: differentiation and quantitative recognition

Weld root crack represents a significant concern in terms of fatigue on orthotropic steel decks (OSDs). This study presents a crack detection method based on ultrasonic penetration signals, to effectively distinguish and quantify cracks in decks. Firstly, based on the principles of sound diffusion and the structural characteristics of OSDs, the parameters were established. Then, the propagation mechanism of ultrasonic waves in decks containing cracks was investigated by finite element analysis. The variations in received signal intensity caused by root-deck and root-weld crack parameters were analyzed and the methods for distinguishing between crack types and determining characteristics were proposed. Finally, experimental tests were conducted on weld root cracks to validate the feasibility of the detection method. The research findings demonstrate that the selected detection parameters adequately meet the requirements for crack detection. By utilizing fitting functions between the transmission signals from two sets of detection parameters and the projected crack length, the method achieves accurate identification of crack length and angle, with an error rate of less than 10%.

Road surface crack detection method based on improved YOLOv5 and vehicle-mounted images

Road surface crack detection methods using vehicle-mounted images have gained substantial attention recently. Notably, YOLO-based techniques have exhibited effectiveness and real-time performance. However, current YOLO-based approaches encounter challenges like blurriness of small cracks and incomplete information extraction from vehicle-mounted images. Therefore, this paper proposes a novel detection method based on the improved YOLOv5 and vehicle-mounted images. In this method, the Slim-Neck structure enhances crack focus through a weighted attention mechanism while optimizing network efficiency. The integration of the C2f structure and Decoupled Head better harnesses upper layer output information. Moreover, the SPPCSPC structure is bifurcated to augment model efficiency and accuracy. The training process is optimized by using the Silu activation function and CIoU loss function. This approach is applied to vehicle-mounted images, with its efficacy and feasibility affirmed through extensive comparative and ablation experiments. Importantly, compared to five other advanced methods, notable enhancements are observed in various evaluation metrics.

Road surface crack detection based on improved YOLOv5s.

In response to the issues of low efficiency and high cost in traditional manual methods for road surface crack detection, an improved YOLOv5s (you only look once version 5 small) algorithm was proposed. Based on this improvement, a road surface crack object recognition model was established using YOLOv5s. First, based on the Res2Net (a new multi-scale backbone architecture) network, an improved multi-scale Res2-C3 (a new multi-scale backbone architecture of C3) module was suggested to enhance feature extraction performance. Second, the feature fusion network and backbone of YOLOv5 were merged with the GAM (global attention mechanism) attention mechanism, reducing information dispersion and enhancing the interaction of global dimensions features. We incorporated dynamic snake convolution into the feature fusion network section to enhance the model's ability to handle irregular shapes and deformation problems. Experimental results showed that the final revision of the model dramatically increased both the detection speed and the accuracy of road surface identification. The mean average precision (mAP) reached 93.9%, with an average precision improvement of 12.6% compared to the YOLOv5s model. The frames per second (FPS) value was 49.97. The difficulties of low accuracy and slow speed in road surface fracture identification were effectively addressed by the modified model, demonstrating that the enhanced model achieved relatively high accuracy while maintaining inference speed.

A Pavement Crack Detection Method via Deep Learning and a Binocular-Vision-Based Unmanned Aerial Vehicle

This study aims to enhance pavement crack detection methods by integrating unmanned aerial vehicles (UAVs) with deep learning techniques. Current methods encounter challenges such as low accuracy, limited efficiency, and constrained application scenarios. We introduce an innovative approach that employs a UAV equipped with a binocular camera for identifying pavement surface cracks. This method is augmented by a binocular ranging algorithm combined with edge detection and skeleton extraction algorithms, enabling the quantification of crack widths without necessitating a preset shooting distance—a notable limitation in existing UAV crack detection applications. We developed an optimized model to enhance detection accuracy, incorporating the YOLOv5s network with an Efficient Channel Attention (ECA) mechanism. This model features a decoupled head structure, replacing the original coupled head structure to optimize detection performance, and utilizes a Generalized Intersection over Union (GIoU) loss function for refined bounding box predictions. Post identification, images within the bounding boxes are segmented by the Unet++ network to accurately quantify cracks. The efficacy of the proposed method was validated on roads in complex environments, achieving a mean Average Precision (mAP) of 86.32% for crack identification and localization with the improved model. This represents a 5.30% increase in the mAP and a 6.25% increase in recall compared to the baseline network. Quantitative results indicate that the measurement error margin for crack widths was 10%, fulfilling the practical requirements for pavement crack quantification.

Quantitative Detection of Pipeline Cracks Based on Ultrasonic Guided Waves and Convolutional Neural Network.

In this study, a quantitative detection method of pipeline cracks based on a one-dimensional convolutional neural network (1D-CNN) was developed using the time-domain signal of ultrasonic guided waves and the crack size of the pipeline as the input and output, respectively. Pipeline ultrasonic guided wave detection signals under different crack defect conditions were obtained via numerical simulations and experiments, and these signals were input as features into a multi-layer perceptron and one-dimensional convolutional neural network (1D-CNN) for training. The results revealed that the 1D-CNN performed better in the quantitative analysis of pipeline crack defects, with an error of less than 2% in the simulated and experimental data, and it could effectively evaluate the size of crack defects from the echo signals under different frequency excitations. Thus, by combining the ultrasonic guided wave detection technology and CNN, a quantitative analysis of pipeline crack defects can be effectively realized.

An adaptable rotated bounding box method for automatic detection of arbitrary-oriented cracks

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

An modified intelligent real-time crack detection method for bridge based on improved target detection algorithm and transfer learning

The combination of UAV camera and intelligent algorithm is a promising method for non-contact bridge crack detection. In this paper, an inspection tool based on UAV Image Acquisition Technology (UAVIAT) and Improved Intelligent Target Detection Technology (IITDT) called Improved Intelligent Real-Time Crack Detection Method for Bridges (IIRTCDMB) is proposed for efficient crack detection. The contributions of this paper are (1) The Squeeze-Excitement (SE) attention module is integrated into the target detection algorithm - You Only Look Once version 7 (YOLOv7) model to improve the learning ability of the feature channel. A Focal-efficient intersection over union (Focal-EIoU) loss function is also introduced to improve the regression accuracy of the model. As a result, a new crack image detection algorithm, YOLOv7-CD, is proposed. (2) A training process based on two-stage transfer learning (TSTL) is established, and hyper-parameter optimization of YOLOv7-CD is carried out. The feasibility and excellent performance of the proposed method are verified by applying it on the Cuntan Yangtze River Bridge. The results show that the average precision (AP) of the YOLOv7-CD model is improved by 3.19% compared with the original YOLOv7 model. After TSTL and hyperparameter optimization, the AP of the YOLOv7-CD model for bridge crack detection reaches 98.01%, which is higher than that of the popular target detection models. The IIRTCDMB proposed in this paper can acquire bridge surface images more safely and efficiently, and provide inspectors with more accurate structural crack information with lower computational and hardware requirements, which can provide technical support for the assessment of structural safety conditions and the formulation of maintenance programs.