Crack Detection Method Research Articles

Deep learning algorithm for real-time automatic crack detection, segmentation, qualification

Cracking is one of the typical damages in concrete structures, and it is crucial to detect and quantify cracks in a timely and efficient manner. However, current research primarily focuses on either single-task recognition or dual-task recognition based on multi-step sequential approaches. Less attention has been devoted to the multi-task integration of cracks. To address the challenges of inefficient and multi-step detection in traditional concrete crack detection methods, a novel deep learning-based model, called YOLOv5-IDS, is proposed based on You Only Look Once network v5 (version 6.2) with the combination of bilateral segmentation network while introducing a dilated convolution, pyramid pooling module, and attention refinement module. Moreover, crack parameter measurement algorithms based on the micro-element method are proposed to improve accuracy and efficiency. The method proposed in this study can not only detect and segment cracks with high accuracy and efficiency, but also quickly measure crack parameters, thus developing a complete method for the process from real-time crack detection and segmentation to crack parameter measurement. The experimental results for the YOLOv5-IDS model reveal the following performance metrics. For crack detection, the mean average precision with an intersection of union threshold of 0.5 (mAP@0.5) is 84.33%, and the frames per second (FPS) is 159 f/s. For crack segmentation, the mean intersection over union (mIoU) is 94.78%, and the FPS is 8 f/s, respectively. Compared to existing methods, the proposed approach exhibits improvements in both accuracy and efficiency. Moreover, the calculation of crack parameters proves to be both precise and rapid.

Infrared image segmentation of cracks in the rail foot

With the rapid development of socio-economy, railway safety operation has attracted increasing attention. Currently, the most widely used crack detection method on railways is still ultrasonic testing. However, the traditional ultrasonic testing method has certain blind zones and cannot detect cracks on both sides of the rail foot. This paper uses infrared non-destructive testing equipment to detect the temperature distribution on the surface of the rail foot, obtains the infrared image of the temperature distribution on the surface of the rail foot, and uses iterative threshold segmentation method to segment the infrared image. The skeleton of rail cracks is extracted from the temperature information of the infrared image, and the length of rail cracks is calculated based on the coordinate temperature information at the edge of the rail. This improves the accuracy of calculating the geometric characteristics of rail foot cracks.

Damage analysis in composite materials using a hybrid artificial immune system

Structural health diagnosis and condition monitoring of structures and components meant for aerospace industries is a challenging task for researchers and aerospace scientists. Presence of a minute crack in a structural element causes variation in local stiffness. The variations of local stiffness depend on crack location and crack depth. Change in stiffness in consequence affects the vibration behaviour of whole structural member. These abnormities in the structures if sustain over a long period cause catastrophic failure. Unexpected failure of any components of space vehicles results huge loss of life, more casualty and affect the economy of a country. To stay away from such situations, regular monitoring on safe operation of structures is extremely essential. Development of efficient damage detection methods is vital for early stage crack detection and its severity. In this work a novel online crack detection method based on artificial immune system has proposed. This article presents analytical and numerical (Finite element method) analysis for crack detection in a hybrid carbon fiber/ kevlar reinforced epoxy composite cantilever beam under free vibration. From the analysis, by varying crack sites and crack depth, alteration in natural frequencies and corresponding mode shapes curvature are recorded. Data (first three natural frequencies) from FEA has trained in negative selection algorithm (NSA). The result of FEA and NSA has compared. Then total average percentage of error is calculated. One statistical method has been used to make NSA more adaptive for the online damage detection. For this purpose a data mining technique named as “Regression Analysis” (RA) is used to reduce residual errors during the data acquisition method. The results are compared with standalone NSA and flexible NSA. Total average percentage of error has calculated.

PSNet: Parallel-Convolution-Based U-Net for Crack Detection with Self-Gated Attention Block

Crack detection is an important task for road maintenance. Currently, convolutional neural-network-based segmentation models with attention blocks have achieved promising results, for the reason that these models can avoid the interference of lights and shadows. However, by carefully examining the structure of these models, we found that these segmentation models usually use down-sampling operations to extract high-level features. This operation reduces the resolution of features and causes feature information loss. Thus, in our proposed method, a Parallel Convolution Module (PCM) was designed to avoid feature information loss caused by down-sampling. In addition, the attention blocks in these models only focused on selecting channel features or spatial features, without controlling feature information flow. To solve the problem, a Self-Gated Attention Block (SGAB) was used to control the feature information flow in the attention block. Therefore, based on the ideas above, a PSNet with a PCM and SGAB was proposed by us. Additionally, as there were few public datasets for detailed evaluation of our method, we collected a large dataset by ourselves, which we named the OAD_CRACK dataset. Compared with the state-of-the-art crack detection method, our proposed PSNet demonstrated competitive segmentation performance. The experimental results showed that our PSNet could achieve accuracies of 92.6%, 81.2%, 98.5%, and 76.2% against the Cracktree200, CRACK500, CFD, and OAD_CRACK datasets, respectively, which were 2.6%, 4.2%, 1.2%, and 3.3% higher than those of the traditional attention models.

A Rapid Bridge Crack Detection Method Based on Deep Learning

The aim of this study is to enhance the efficiency and lower the expense of detecting cracks in large-scale concrete structures. A rapid crack detection method based on deep learning is proposed. A large number of artificial samples from existing concrete crack images were generated by a deep convolutional generative adversarial network (DCGAN), and the artificial samples were balanced and feature-rich. Then, the dataset was established by mixing the artificial samples with the original samples. You Only Look Once v5 (YOLOv5) was trained on this dataset to implement rapid detection of concrete bridge cracks, and the detection accuracy was compared with the results using only the original samples. The experiments show that DCGAN can mine the potential distribution of image data and extract crack features through the deep transposed convolution layer and down sampling operation. Moreover, the light-weight YOLOv5 increases channel capacity and reduces the dimensions of the input image without losing pixel information. This method maintains the generalization performance of the neural network and provides an alternative solution with a low cost of data acquisition while accomplishing the rapid detection of bridge cracks with high precision.

Deep Learning-Based Subsurface Damage Localization Using Full-Field Surface Strains.

Structures in their service life are often damaged as a result of aging or extreme events such as earthquakes or storms. It is essential to detect damage in a timely fashion to ensure the safe operation of the structure. If left unchecked, subsurface damage (SSD) can cause significant internal damage and may result in premature structural failure. In this study, a Convolutional Neural Network (CNN) has been developed for SSD detection using surface strain measurements. The adopted network architecture is capable of pixel-level image segmentation, that is, it classifies each location of strain measurement as damaged or undamaged. The CNN which is fed full-field strain measurements as an input image of size 256 × 256 projects the SSD onto an output image of the same size. The data for network training is generated by numerical simulation of aluminum bars with different damage scenarios, including single damage and double damage cases at a random location, direction, length, and thickness. The trained network achieves an Intersection over Union (IoU) score of 0.790 for the validation set and 0.794 for the testing set. To check the applicability of the trained network on materials other than aluminum, testing is performed on a numerically generated steel dataset. The IoU score is 0.793, the same as the aluminum dataset, affirming the network's capability to apply to materials exhibiting a similar stress-strain relationship. To check the generalization potential of the network, it is tested on triple damage cases; the IoU score is found to be 0.764, suggesting that the network works well for unseen damage patterns as well. The network was also found to provide accurate predictions for real experimental data obtained from Strain Sensing Smart Skin (S4). This proves the efficacy of the network to work in real-life scenarios utilizing the full potential of the novel full-field strain sensing methods such as S4. The performance of the proposed network affirms that it can be used as a non-destructive testing method for subsurface crack detection and localization.

Wooden spoon crack detection by prior knowledge-enriched deep convolutional network

Since the outbreak of COVID-19, in order to reduce people’s contact, the takeaway business has been developed rapidly, bringing a large demand for disposable and degradable tableware (e.g., wooden spoon). However, in the production process of wooden spoon, the selection of crack spoons still relies on manual labour. Therefore, in order to detect cracked wooden spoons more effectively and reduce production costs, we propose a wooden spoon crack detection method by using machine vision techniques and apply it in real-world industrial factory. In the production system, the captured color of crack regions is black while the good region shows normal log color. The positions of crack regions are located frequently in the central or marginal areas of spoons and their directions of cracks are often same due to the extrusion of the mold in the production process. Based on these two types of prior knowledge (i.e., color and spatial prior information), three modules are designed to explore these priors by jointly integrating with the current mainstream detection network of YOLO-v5, which satisfies the speed and accuracy for detecting cracks. The color fusion module is designed to explore the color difference between good regions and crack regions. The attention and orientation modules are then combined and embedded into the backbone of deep architecture. Reported experiments on our collected database show that our proposed detection method can locate the spoon cracks very well and significantly outperforms the model of YOLO-v5 with the protocols of Recall, Precision and meanAveragePrecision(mAP).

Relative-Breakpoint-Based Crack Annotation Method for Lightweight Crack Identification Using Deep Learning Methods

After years of service, bridges could lose their expected functions. Considering the significant number of bridges and the adverse inspecting environment, the urgent requirement for timely and efficient inspection solutions, such as computer vision techniques, have been attractive in recent years, especially for those bridge components with poor accessibility. In this paper, a lightweight procedure for bridge apparent-defect detection is proposed, including a crack annotation method and crack detection. First of all, in order to save computational costs and improve generalization performance, we propose herein a relative-breakpoint annotation method to build a crack instance segmentation dataset, a critical process for a supervised vision-based crack detection method. Then, the trained models based on classic Mask RCNN and Yolact are transferred to evaluate the effectiveness of the proposed method. To verify the correctness, universality and generality of the proposed crack-detection framework, approximately 800 images are used for model training, while nearly 100 images are saved for validation. Results show that the crack instance segmentation model can achieve a level of 90% for both accuracy and recall values, with a limited dataset.

Research on Intelligent Recognition Method of Egg cracks Based on EfficientNet Network Model

Aiming at the egg crack detection task, a 6-position egg image acquisition method is proposed. EfficientNet was used to classify egg cracks. Using transfer learning technology, the model is pre-trained with CIFAR-10 data set to obtain initial weights. The efficiency of model training is improved. Compared with the current mainstream CNN models of Alexnet, VGG16 and ResNet50, in terms of egg crack recognition, the average correct recognition rate of EfficientNet model is as high as 98.6 %, which is significantly better than Alexnet, VGG16 and ResNet50 models. Among them, the correct recognition rate of EfficientNetB2 model is 99.5 %, the training time is only 29 min, and the comprehensive performance is the best. It is the most suitable method for egg crack detection. Finally, three learning rates are set for the EfficientNetB2 model. The experimental results show that the model has the best performance when the learning rate is 0.001. The improved model proposed in this paper improves the accuracy of egg crack detection and reduces the model training time.

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

AbstractCracks in walnuts during processing and storage can adversely affect their quality and cause economic losses. To achieve efficient identification of cracked walnuts, this study proposed a method of walnut crack identification based on acoustic vibration and feature fusion. First, the sound signals of intact and cracked walnuts were collected using an acoustic signal acquisition system, and 44 time‐domain features, 13 frequency‐domain features, and 768 Mel spectrogram features (the number of pixel frequencies corresponding to the gray‐scale values of R, G, and B channels) of the sound signals were extracted. Then, the classification models of support vector machines (SVM), least squares support vector machines (LSSVM), and extreme learning machines (ELM) were established based on single class features data and fusion of different feature groups data respectively. The results indicated that the LSSVM model with the fusion of the three feature sets was optimal, with an accuracy of 85% in the testing set. Next, three feature selection methods were employed to reduce the dimensionality of the best fused feature data. Subsequently, the LSSVM classification model was established based on the feature selection data. Finally, arithmetic optimization algorithm (AOA), particle swarm optimization (PSO), and gray wolf optimization (GWO) were introduced to optimize the parameters c and of the classification model. The results indicated that the best classification model was VISSA‐IRIV‐GWO‐LSSVM, with 95% accuracy in the testing set. This study provides theoretical support for the research and development of online detection equipment for walnut crack in Yunnan Yangbi.Practical applicationsCracks in walnut during harvesting, transportation and peeling may lead to economic losses and food safety problems. Aiming at the difficulty and low accuracy of crack identification in Yunnan walnut, this paper proposed a method for crack detection based on acoustic vibration identification combine with feature fusion. The features of time domain, frequency domain and Mel spectrogram were extracted from the effective sound signal, and the features were fused. The influence of three feature selection methods, three models and three optimization algorithms on walnut sound signal recognition was analyzed and compared. The results indicated that the best classification model was VISSA‐IRIV‐GWO‐LSSVM, and the method proposed in this study provides theoretical support for the research and development of online detection equipment for walnut crack in Yunnan Yangbi.

Automatic identification of pavement cracks in public roads using an optimized deep convolutional neural network model.

The current study aims to improve the efficiency of automatic identification of pavement distress and improve the status quo of difficult identification and detection of pavement distress. First, the identification method of pavement distress and the types of pavement distress are analysed. Then, the design concept of deep learning in pavement distress recognition is described. Finally, the mask region-based convolutional neural network (Mask R-CNN) model is designed and applied in the recognition of road crack distress. The results show that in the evaluation of the model's comprehensive recognition performance, the highest accuracy is 99%, and the lowest accuracy is 95% after the test and evaluation of the designed model in different datasets. In the evaluation of different crack identification and detection methods, the highest accuracy of transverse crack detection is 98% and the lowest accuracy is 95%. In longitudinal crack detection, the highest accuracy is 98% and the lowest accuracy is 92%. In mesh crack detection, the highest accuracy is 98% and the lowest accuracy is 92%. This work not only provides an in-depth reference for the application of deep CNNs in pavement distress recognition but also promotes the improvement of road traffic conditions, thus contributing to the progression of smart cities in the future. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Method and Research of Concrete Pavement Crack Detection Based on Convolution Neural Network

Method and Research of Concrete Pavement Crack Detection Based on Convolution Neural Network

Crack Detection of Bridge Concrete Components Based on Large-Scene Images Using an Unmanned Aerial Vehicle.

The current method of crack detection in bridges using unmanned aerial vehicles (UAVs) relies heavily on acquiring local images of bridge concrete components, making image acquisition inefficient. To address this, we propose a crack detection method that utilizes large-scene images acquired by a UAV. First, our approach involves designing a UAV-based scheme for acquiring large-scene images of bridges, followed by processing these images using a background denoising algorithm. Subsequently, we use a maximum crack width calculation algorithm that is based on the region of interest and the maximum inscribed circle. Finally, we applied the method to a typical reinforced concrete bridge. The results show that the large-scene images are only 1/9-1/22 of the local images for this bridge, which significantly improves detection efficiency. Moreover, the accuracy of the crack detection can reach up to 93.4%.

An active learning method for crack detection based on subset searching and weighted sampling

Active learning is an important technology to solve the lack of data in crack detection model training. However, the sampling strategies of most existing active learning methods for crack detection are based on the uncertainty or representation of the samples, which cannot effectively balance the exploitation and exploration of active learning. To solve this problem, this study proposes an active learning method for crack detection based on subset searching and weighted sampling. First, a new active learning framework is established to successively search subsets with large uncertainty from the candidate dataset, and select training samples with large diversity from the subsets to update the crack detection model. Second, to realize the active learning process, a subset searching method based on sample relative error is proposed to adaptively select subsets with large uncertainty, and a weighted sampling method based on flow-based deep generative network is introduced to select training samples with large diversity form the subsets. Third, a termination criterion for active learning directly based on the prediction accuracy of the trained model is proposed to adaptively determine the maximum number of iterations. Finally, the proposed method is tested using two open-source crack datasets. The experimental comparison results on the Bridge Crack Library dataset show that the proposed method has higher calculation efficiency and prediction accuracy in crack detection than the uncertainty-based and representation-based active learning methods. The test results on the DeepCrack dataset show that the crack detection model trained by the proposed method has good transferability on different datasets with multi-scale concrete cracks and scenes.

Pavement Cracks Coupled With Shadows: A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach

Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety. The task is challenging because the shadows on the pavement may have similar intensity with the crack, which interfere with the crack detection performance. Till to the present, there still lacks efficient algorithm models and training datasets to deal with the interference brought by the shadows. To fill in the gap, we made several contributions as follows. First, we proposed a new pavement shadow and crack dataset, which contains a variety of shadow and pavement pixel size combinations. It also covers all common cracks (linear cracks and network cracks), placing higher demands on crack detection methods. Second, we designed a two-step shadow-removal-oriented crack detection approach: SROCD, which improves the performance of the algorithm by first removing the shadow and then detecting it. In addition to shadows, the method can cope with other noise disturbances. Third, we explored the mechanism of how shadows affect crack detection. Based on this mechanism, we propose a data augmentation method based on the difference in brightness values, which can adapt to brightness changes caused by seasonal and weather changes. Finally, we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters, and the algorithm improves the performance of the model overall. We compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset, and the experimental results demonstrate the superiority of our method.

Pavement Crack Detection Based on 3D Edge Representation and Data Communication With Digital Twins

With digital information applied in intelligent transportation system, pavement crack detection with digital twins has drawn widely attention since the past several years. However, it is still a challenge task to accomplish crack detection satisfactory results due to complex pavement background. This paper presents a pavement crack detection method based on 3D edge representation and data communication with digital twins. Firstly, pavement data is achieved by multiple sensors including video data and 3D scanning technology, and the digital twins based model is constructed based on data communication. Secondly, to distinguish between crack feature and pavement background in depth level, the point cloud data enhancement method based on fractional differential is proposed to strengthen crack boundary and maintain detail simultaneously. Thirdly, a 3D crack segmentation algorithm is proposed based on edge feature representation. After improved filter function with plane proximity and depth similarity, the gradient magnitude values of every point are estimated by the neighborhood and direction. Two thresholds are computed automatically using the iteration method to extract the crack from pavement. Finally, the dataset is established by 2600 crack data, and extensive experiments verify the proposed method could conduct better result compared with other existing methods using F-measure evaluation system.

Pixel-level road crack detection in UAV remote sensing images based on ARD-Unet

Crack is the main manifestation of road damage, and its further deterioration will affect road traffic. The timely detection of road cracks is of great significance for ensuring road safety. In this work, starting from UAV remote sensing images,a pixel-by-pixel crack detection method named ARD-Unet is proposed based on U-Net combined with Depth Separable Residual Block (DR-Block), Atrous Spatial Pyramid Fusion Attention Module (ASAM) and Receptive Field Block (RFB). We used UAV to construct a remote sensing road crack dataset containing 1046 high-quality images. The proposed method achieves 76.41 % MIOU and 74.24 % F1-Score on the self-made dataset. Finally, we combine ARD-Unet with UAV to build a road crack detection UAV IoT system, which has been tested in practical applications and achieved excellent performance. Experiments show that ARD-Unet is effective for road crack detection in remote sensing images.

Research on Infrared Image Fusion Technology Based on Road Crack Detection

This study aimed to propose road crack detection method based on infrared image fusion technology. By analyzing the characteristics of road crack images, this method uses a variety of infrared image fusion methods to process different types of images. The use of this method allows the detection of road cracks, which not only reduces the professional requirements for inspectors, but also improves the accuracy of road crack detection. Based on infrared image processing technology, on the basis of in-depth analysis of infrared image features, a road crack detection method is proposed, which can accurately identify the road crack location, direction, length, and other characteristic information. Experiments showed that this method has a good effect, and can meet the requirement of road crack detection.

Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem

This paper presents the development of an underwater crack detection system for structural integrity assessment of submerged structures, such as offshore oil and gas installations, underwater pipelines, underwater foundations for bridges, dams, etc. Our focus is on the use of machine-learning-based approaches. First, a detailed literature review of the state of the current methods for underwater surface crack detection is presented, highlighting challenges and opportunities. An overview of the image augmentation approach for the creation of underwater optical effects is also presented. Experimental results using a standard network-based machine learning approach, which is used for surface crack detection in onshore environments, are presented. A series of test cases is presented in which existing networks’ performance is improved using augmented images for underwater conditions. The effectiveness and accuracy of the proposed approach in detecting cracks in underwater concrete structures are demonstrated. The proposed approach has the potential to improve the safety and reliability of underwater structures and prevent catastrophic failures.

One-stage 3D profile-based pavement crack detection and quantification

To improve the robustness and efficiency of pavement crack inspection tasks, a one-stage pavement crack detection and quantification method is proposed to directly extract crack geometry from 3D pavement profile. A low-cost stereo imaging system is implemented for high-resolution 3D reconstruction. Based on the generated 3D profile, an algorithm that integrates crack detection and quantification is developed to obtain the crack map, length, width, and depth simultaneously. The results show that the proposed method is robust to surrounding noises and has no demand for prior data. Compared with deep learning-based crack detection methods, the mean IoU of the proposed method is 6.23% higher than that of state-of-the-art methods. The proposed method outperforms existing methods in terms of crack length and width measurement. Moreover, the proposed method automatically measures crack depth with a correlation coefficient R2 of 0.9679 against the manual measurement.