Automatic Crack Detection Research Articles

MobileNetV3-BLS: A broad learning approach for automatic concrete surface crack detection

Concrete surface crack detection is an important means to ensure infrastructure security, including bridges and tunnels. In recent years, computer vision has been widely adopted for concrete surface crack detection, and the intelligent crack detection method based on computer vision has achieved good results with the breakthrough of the deep learning algorithm. However, there are also some challenges, such as complex architecture, high computing burden, and time-consuming training. In this paper, a lightweight broad learning system detection method called MobileNetV3-BLS is proposed. First, the inverted residual structure with the linear bottleneck of MobileNetV3 is introduced as the convolution module of the proposed broad learning system for feature extraction. Then the extracted features are employed to randomly generate both mapping and enhancement nodes in a broad learning system, which are used to train the crack detection model. The proposed MobileNetV3-BLS can improve crack detection accuracy and training speed compared with other classical deep learning networks. On the other hand, the proposed method is a dynamic network of which the weights can be updated quickly to achieve incremental learning by adding new nodes and samples.

A novel transformer-based network with attention mechanism for automatic pavement crack detection

Currently, there is an urgent need to utilize automatic approaches to detecting pavement cracks for roadway maintenance. Taking advantage of the development of convolutional neural networks (CNNs), previous studies put more effort into the detection of the pavement crack with local feature extraction using consecutive convolutional operations. However, it results in the loss of detailed information, making CNNs fail to accurately inspect the long and complicated cracks under noisy conditions, which are common on the pavement surface, negatively impacting detection accuracy. In order to cope with this issue, this study proposes a Transformer-based semantic segmentation network that unifies the Swin Transformer as the Encoder and the UperNet with the attention module as the Decoder for robust and accurate pixel-level pavement crack detection. Leveraging the hierarchical architecture of Swin Transformer, the global and long-range semantic features of the pavement crack are learned for improved segmentation accuracy. With the assistance of the attention module, the Decoder can retrieve more details of the crack information, presenting accurate detection results on the fine and tiny pavement cracks. To validate the superiority of the proposed network, we have trained and tested six semantic segmentation models on three public pavement crack datasets. Compared to other models, the proposed model achieves the best performance on visualization and evaluation metrics of mean F1(mF1) and mean Recall (mRecall) with 0-pixel tolerance. It paves the way for future applications of automatic pavement crack detection using Transformed-based networks.

A System for the Automatic Detection and Evaluation of the Runway Surface Cracks Obtained by Unmanned Aerial Vehicle Imagery Using Deep Convolutional Neural Networks

The timely detection and recognizing of distress on an airport pavement is crucial for safe air traffic. For this purpose, a physical inspection of the airport maneuvering areas is regularly carried out, which might be time-consuming due to its size. One of the modern approaches to speeding up this process is unmanned aerial vehicle imagery followed by an automatic evaluation. This study explores the automatic detection of the transverse crack, its dimension measurement, and position determination within the slab on the concrete runway. The aerial image data were obtained from flights at the given altitude above the runway and processed using commercial multi-view reconstruction software to create a dataset for the training, verification, and testing of a YOLOv2 object detector. Once the crack was detected, the main features were obtained by image segmentation and morphological operations. The YOLOv2 detector was tuned with 3279 images until the detection metrics (average precision AP = 0.89) reached sufficient value for real deployment. The detected cracks were further processed to determine their position within the concrete slab, and their dimensions, i.e., length and width, were measured. The automated crack detection and evaluation system developed in this study was successfully verified on the experimental section of the runway as an example of practical application. It was proven that unmanned aerial vehicle imagery is efficient over broad areas and produces impressive results with the combination of artificial intelligence.

Development of Railway Track Crack Detection System using Arduino

The Indian Railways has one of the largest railway networks in the world, criss- crossing over 1,15,000 km in distance, all over India. However, with regard to reliability and passenger safety Indian Railways is not up to global standards. Among other factors, cracks developed on the rails due to absence of timely detection and the associated maintenance pose serious questions on the security of operation of rail transport. A recent study revealed that over 25% of the track length is in need of replacement due to the development of cracks on it. Manual detection of tracks is cumbersome and not fully effective owing to much time consumption and requirement of skilled technicians. This paper is aimed towards addressing the issue by developing an automatic railway track crack detection system with the proliferation of Internet of Things (IoT). It is possible to collect massive amount of data for localization and tracking of Railway Fault Detection.

Augmented reality-computer vision combination for automatic fatigue crack detection and localization

Fatigue cracks in bridges are inspected visually by specialized bridge inspection professionals. Bridge inspectors conduct inspections in a limited amount of time, and at times small cracks may go unnoticed. Researchers have recently developed computer-based techniques to help overcome these issues. This study uses a computer vision algorithm combined with Augmented Reality (AR) to localize fatigue cracks during the visual inspection that otherwise may go unnoticed because of their size. The AR software utilizes a video processing algorithm for fatigue crack detection. Subsequently, the AR software generates a hologram using the algorithm’s detection result and anchors it over the crack’s location at the structure being inspected. The result of this new methodology is an automatic fatigue crack detection and localization AR software that provides holograms overlaid during the on-site visual inspection. This technique also provides the fatigue crack detection result in near-real-time. The method is verified using 2D and 3D benchmarks and a half-scale steel bridge girder specimen. The research team collected the feedback from bridge industry inspectors contextualized in real operations and their recommendations for integrating AR for inspections with the involvement of human resources and workforce development. This study is the first effort of holographic addressing the fatigue crack on the actual structure using AR headset.

Automated detection of propagating cracks in RC beams without shear reinforcement based on DIC-controlled modeling of damage localization

In spite of an extensive research in the past few decades, design of beams without shear reinforcement is still considered to be a challenging task. With the goal of developing realistic and simple engineering models, many researchers have contributed to the development of analytical and numerical approaches that have improved the understanding of the mechanical background of shear fracture. Still, some assumptions in these approaches, such as the rigid body kinematics which relies on the assumption of a uniquely identifiable center of rotation, have raised critical questions in the scientific community that need further consideration. In this paper, an automated crack detection scheme based on DIC-controlled modeling of damage localization is developed and presented. This scheme enables an automatic evaluation of opening and sliding profiles along the crack ligament, the center of rotation between two neighboring concrete teeth, and the moment–rotation curve directly from the DIC measurements of the surface displacement. To demonstrate the feasibility and the features of the developed scheme, verification studies are performed using tests performed on longitudinally reinforced concrete beams. The description of the mathematical concepts behind the developed crack detection scheme is accompanied with the explanation of the implementation method using modern Python packages for scientific computing, providing code snippets that show a straightforward translation of the mathematics into an executable code. Furthermore, a publicly available implementation of the developed scheme is provided in the form of an interactive Jupyter notebook.

An automated crack detection method for underwater structures based on multilevel DWT and LPQ feature generation

An automated crack detection method for underwater structures based on multilevel DWT and LPQ feature generation

Automatic Pavement Crack Detection Transformer Based on Convolutional and Sequential Feature Fusion

To solve the problem of low accuracy of pavement crack detection caused by natural environment interference, this paper designed a lightweight detection framework named PCDETR (Pavement Crack DEtection TRansformer) network, based on the fusion of the convolution features with the sequence features and proposed an efficient pavement crack detection method. Firstly, the scalable Swin-Transformer network and the residual network are used as two parallel channels of the backbone network to extract the long-sequence global features and the underlying visual local features of the pavement cracks, respectively, which are concatenated and fused to enrich the extracted feature information. Then, the encoder and decoder of the transformer detection framework are optimized; the location and category information of the pavement cracks can be obtained directly using the set prediction, which provided a low-code method to reduce the implementation complexity. The research result shows that the highest AP (Average Precision) of this method reaches 45.8% on the COCO dataset, which is significantly higher than that of DETR and its variants model Conditional DETR where the AP values are 36.9% and 42.8%, respectively. On the self-collected pavement crack dataset, the AP of the proposed method reaches 45.6%, which is 3.8% higher than that of Mask R-CNN (Region-based Convolution Neural Network) and 8.8% higher than that of Faster R-CNN. Therefore, this method is an efficient pavement crack detection algorithm.

A Low-Cost Deep Learning System to Characterize Asphalt Surface Deterioration

Every day millions of people travel on highways for work- or leisure-related purposes. Ensuring road safety is thus of paramount importance, and maintaining good-quality road pavements is essential, requiring an effective maintenance policy. The automation of some road pavement maintenance tasks can reduce the time and effort required from experts. This paper proposes a simple system to help speed up road pavement surface inspection and its analysis towards making maintenance decisions. A low-cost video camera mounted on a vehicle was used to capture pavement imagery, which was fed to an automatic crack detection and classification system based on deep neural networks. The system provided two types of output: (i) a cracking percentage per road segment, providing an alert to areas that require attention from the experts; (ii) a segmentation map highlighting which areas of the road pavement surface are affected by cracking. With this data, it became possible to select which maintenance or rehabilitation processes the road pavement required. The system achieved promising results in the analysis of highway pavements, and being automated and having a low processing time, the system is expected to be an effective aid for experts dealing with road pavement maintenance.

An Integrated Method for Road Crack Segmentation and Surface Feature Quantification under Complex Backgrounds

In the present study, an integrated framework for automatic detection, segmentation, and measurement of road surface cracks is proposed. First, road images are captured, and crack regions are detected based on the fifth version of the You Only Look Once (YOLOv5) algorithm; then, a modified Residual Unity Networking (Res-UNet) algorithm is proposed for accurate segmentation at the pixel level within the crack regions; finally, a novel crack surface feature quantification algorithm is developed to determine the pixels of crack in width and length, respectively. In addition, a road crack dataset containing complex environmental noise is produced. Different shooting distances, angles, and lighting conditions are considered. Validated through the same dataset and compared with You Only Look at CoefficienTs ++ (YOLACT++) and DeepLabv3+, the proposed method shows higher accuracy for crack segmentation under complex backgrounds. Specifically, the crack damage detection based on the YOLOv5 method achieves a mean average precision of 91%; the modified Res-UNet achieves 87% intersection over union (IoU) when segmenting crack pixels, 6.7% higher than the original Res-UNet; and the developed crack surface feature algorithm has an accuracy of 95% in identifying the crack length and a root mean square error of 2.1 pixels in identifying the crack width, with the accuracy being 3% higher in length measurement than that of the traditional method.

Reference-free fatigue crack detection using deep long short-term memory network (DLSTM) and nonlinear ultrasonic modulation

Nonlinear ultrasonic modulation is sensitive to fatigue crack, but a reference signal or user-specified threshold is often required for crack diagnosis, easily causing false alarms in noisy environments. In this study, a reference-free damage detection method was developed by applying a deep long short-term memory network (DLSTM) to nonlinear ultrasonic modulation signals. First, an ultrasonic signal was generated and measured using piezoceramic ultrasonic transducers. Subsequently, a DLSTM network was constructed and trained to learn the inherent sequential patterns of the measured ultrasonic signals. Then, an absolute damage index (ADI) was defined and computed using only the current ultrasonic signal without any reference ultrasonic signal obtained from the intact condition. Finally, the crack was automatically detected using the ADI and without any user-specified threshold. The performance of the proposed method was examined using data from a submerged floating tunnel model and an actual long-span bridge. The results highlight the feasibility of the proposed method for automatic fatigue crack detection.

A Brief Overview on Crack Patterns, Repair and Strengthening of Historical Masonry Structures.

Given that a significant fraction of buildings and architectural heritage in Europe's historical centers are masonry structures, the selection of proper diagnosis, technological surveys, non-destructive testing, and interpretations of crack and decay patterns is paramount for a risk assessment of possible damage. Identifying the possible crack patterns, discontinuities, and associated brittle failure mechanisms within unreinforced masonry under seismic and gravity actions allows for reliable retrofitting interventions. Traditional and modern materials and strengthening techniques create a wide range of compatible, removable, and sustainable conservation strategies. Steel/timber tie-rods are mainly used to support the horizontal thrust of arches, vaults, and roofs and are particularly suitable for better connecting structural elements, e.g., masonry walls and floors. Composite reinforcing systems using carbon, glass fibers, and thin mortar layers can improve tensile resistance, ultimate strength, and displacement capacity to avoid brittle shear failures. This study overviews masonry structural diagnostics and compares traditional and advanced strengthening techniques of masonry walls, arches, vaults, and columns. Several research results in automatic surface crack detection for unreinforced masonry (URM) walls are presented considering crack detection based on machine learning and deep learning algorithms. In addition, the kinematic and static principles of Limit Analysis within the rigid no-tension model framework are presented. The manuscript sets a practical perspective, providing an inclusive list of papers describing the essential latest research in this field; thus, this paper is useful for researchers and practitioners in masonry structures.

Automated crack detection and crack depth prediction for reinforced concrete structures using deep learning

Automatic inspection for crack detection and estimation of the crack depth is critical in assessing the damage and determining the appropriate method of repair in concrete structures. Most of the studies which have employed deep learning models for automatic inspection are limited to the detection and estimation of the width, length, area, and direction of cracks. The innovation of this study lies in developing a comprehensive automated crack detection and crack depth evaluation framework for concrete structures using images taken from portable devices. Firstly, a binary-class Convolutional Neural Network (CNN) model was developed to automatically detect the cracks on a concrete surface. Secondly, an integrated CNN model combining the convolutional feature extraction layers and regression models (RF and XGBoost) was developed to automatically predict the depth of the cracks. The proposed framework has been validated on a reinforced concrete (RC) slab. Results indicate the models are accurate and reliable for automated inspection of the cracks which could help in evaluating the condition of a concrete structure and choosing suitable repair methods.

Automated crack detection in laminated composites by optical flow measurements

In this paper, an automated crack detection method for laminated composites of carbon fiber reinforced polymers (CFRP) is presented. Image correlation is performed on micrographs of the unspeckled specimen edge using DeepFlow, an algorithm for optical flow estimation. Cracks are then detected from local maxima of the displacement gradient evaluated along the midline of the plies. The detection strategy is applied to 3 different materials, with increasingly irregular microstructures, and on quasi-isotropic layups (a challenging case for crack detection). The crack density evolution in the 90∘ and ±45∘ plies is presented. The proposed method proves its capacity to detect cracks, even in complex configurations where other methods fail, and can provide an extensive amount of data that are useful for the identification of damage models for laminates. The procedure is largely automated in order to make it suitable for industrial applications.

Pixel-Level Concrete Crack Segmentation Using Pyramidal Residual Network with Omni-Dimensional Dynamic Convolution

Automated crack detection technologies based on deep learning have been extensively used as one of the indicators of performance degradation of concrete structures. However, there are numerous drawbacks of existing methods in crack segmentation due to the fine and microscopic properties of cracks. Aiming to address this issue, a crack segmentation method is proposed. First, a pyramidal residual network based on encoder–decoder using Omni-Dimensional Dynamic Convolution is suggested to explore the network suitable for the task of crack segmentation. Additionally, the proposed method uses the mean intersection over union as the network evaluation index to lessen the impact of background features on the network performance in the evaluation and adopts a multi-loss calculation of positive and negative sample imbalance to weigh the negative impact of sample imbalance. As a final step in performance evaluation, a dataset for concrete cracks is developed. By using our dataset, the proposed method is validated to have an accuracy of 99.05% and an mIoU of 87.00%. The experimental results demonstrate that the concrete crack segmentation method is superior to the well-known networks, such as SegNet, DeeplabV3+, and Swin-unet.

STrans-YOLOX: Fusing Swin Transformer and YOLOX for Automatic Pavement Crack Detection

Automatic pavement crack detection is crucial for reducing road maintenance costs and ensuring transportation safety. Although convolutional neural networks (CNNs) have been widely used in automatic pavement crack detection, they cannot adequately model the long-range dependencies between pixels and easily lose edge detail information in complex scenes. Moreover, irregular crack shapes also make the detection task challenging. To address these issues, an automatic pavement crack detection architecture named STrans-YOLOX is proposed. Specifically, the architecture first exploits the CNN backbone to extract feature information, preserving the local modeling ability of the CNN. Then, Swin Transformer is introduced to enhance the long-range dependencies through a self-attention mechanism by supplying each pixel with global features. A new global attention guidance module (GAGM) is used to ensure effective information propagation in the feature pyramid network (FPN) by using high-level semantic information to guide the low-level spatial information, thereby enhancing the multi-class and multi-scale features of cracks. During the post-processing stage, we utilize α-IoU-NMS to achieve the accurate suppression of the detection boxes in the case of occlusion and overlapping objects by introducing an adjustable power parameter. The experiments demonstrate that the proposed STrans-YOLOX achieves 63.37% mAP and surpasses the state-of-the-art models on the challenging pavement crack dataset.

Investigation and Analysis of Crack Detection using UAV and CNN: A Case Study of Hospital Raja Permaisuri Bainun

Crack detection in old buildings has been shown to be inefficient, with many technical challenges such as physical inspection and difficult measurements. It is important to have an automatic, fast visual inspection of these building components to detect cracks by evaluating their conditions (impact) and the level of their risk. Unmanned Aerial Vehicles (UAV) can automate, avoid visual inspection, and avoid other physical check-ups of these buildings. Automated crack detection using Machine Learning Algorithms (MLA), especially a Conventional Neural Network (CNN), along with an Unmanned Aerial Vehicle (UAV), can be effective and both can efficiently work together to detect the cracks in buildings using image processing techniques. The purpose of this research project is to evaluate currently available crack detection systems and to develop an automated crack detection system using Aggregate Channel Features (ACF)that can be used with unmanned aerial vehicles (UAV). Therefore, we conducted a real-world experiment of crack detection at Hospital Raja Permaisuri Bainun using DJI Mavic Air (Drone Hardware) and DJI GO 4(Drone Software) using CNN through MATLAB software with CNN-SVM method with the accuracy rate of3.0 percent increased from 82.94%to 85.94%. in comparison with other ML algorithms like CNN Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Network (ANN).

Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

Artificial Intelligence (AI) and allied disruptive technologies have revolutionized the scientific world. However, civil engineering, in general, and infrastructure management, in particular, are lagging behind the technology adoption curves. Crack identification and assessment are important indicators to assess and evaluate the structural health of critical city infrastructures such as bridges. Historically, such critical infrastructure has been monitored through manual visual inspection. This process is costly, time-consuming, and prone to errors as it relies on the inspector’s knowledge and the gadgets’ precision. To save time and cost, automatic crack and damage detection in bridges and similar infrastructure is required to ensure its efficacy and reliability. However, an automated and reliable system does not exist, particularly in developing countries, presenting a gap targeted in this study. Accordingly, we proposed a two-phased deep learning-based framework for smart infrastructure management to assess the conditions of bridges in developing countries. In the first part of the study, we detected cracks in bridges using the dataset from Pakistan and the online-accessible SDNET2018 dataset. You only look once version 5 (YOLOv5) has been used to locate and classify cracks in the dataset images. To determine the main indicators (precision, recall, and mAP (0.5)), we applied each of the YOLOv5 s, m, and l models to the dataset using a ratio of 7:2:1 for training, validation, and testing, respectively. The mAP (Mean average precision) values of all the models were compared to evaluate their performance. The results show mAP values for the test set of the YOLOv5 s, m, and l as 97.8%, 99.3%, and 99.1%, respectively, indicating the superior performance of the YOLOv5 m model compared to the two counterparts. In the second portion of the study, segmentation of the crack is carried out using the U-Net model to acquire their exact pixels. Using the segmentation mask allocated to the attribute extractor, the pixel’s width, height, and area are measured and visualized on scatter plots and Boxplots to segregate different cracks. Furthermore, the segmentation part validated the output of the proposed YOLOv5 models. This study not only located and classified the cracks based on their severity level, but also segmented the crack pixels and measured their width, height, and area per pixel under different lighting conditions. It is one of the few studies targeting low-cost health assessment and damage detection in bridges of developing countries that otherwise struggle with regular maintenance and rehabilitation of such critical infrastructure. The proposed model can be used by local infrastructure monitoring and rehabilitation authorities for regular condition and health assessment of the bridges and similar infrastructure to move towards a smarter and automated damage assessment system.

Assessment of Convolutional Neural Network Pre-Trained Models for Detection and Orientation of Cracks.

Failure due to cracks is a major structural safety issue for engineering constructions. Human examination is the most common method for detecting crack failure, although it is subjective and time-consuming. Inspection of civil engineering structures must include crack detection and categorization as a key component of the process. Images can automatically be classified using convolutional neural networks (CNNs), a subtype of deep learning (DL). For image categorization, a variety of pre-trained CNN architectures are available. This study assesses seven pre-trained neural networks, including GoogLeNet, MobileNet-V2, Inception-V3, ResNet18, ResNet50, ResNet101, and ShuffleNet, for crack detection and categorization. Images are classified as diagonal crack (DC), horizontal crack (HC), uncracked (UC), and vertical crack (VC). Each architecture is trained with 32,000 images equally divided among each class. A total of 100 images from each category are used to test the trained models, and the results are compared. Inception-V3 outperforms all the other models with accuracies of 96%, 94%, 92%, and 96% for DC, HC, UC, and VC classifications, respectively. ResNet101 has the longest training time at 171 min, while ResNet18 has the lowest at 32 min. This research allows the best CNN architecture for automatic detection and orientation of cracks to be selected, based on the accuracy and time taken for the training of the model.

INTELLIGENT 3D CRACK RECONSTRUCTION USING CLOSE RANGE PHOTOGRAMMETRY IMAGERY

Abstract. Civil infrastructure Structural Health Monitoring (SHM) and its preservation from deterioration is a crucial task. In general, natural disasters like severe earthquakes, extreme landslides, subsidence or intensive floods directly influence the health of civil structures such as buildings, bridges, roads, and dams. Evaluation and inspection of defects and damages of the aforementioned structures help to preserve them from destruction by accelerating rehabilitation and reconstruction. An automatic and precise crack detection framework is required for periodic assessment and inspection due to the large number of the structures. In this study, a two-step crack segmentation and its 3D reconstruction procedure is proposed. The crack segmentation is carried out by using Deeplabv3+ architecture and Xception as the backbone. Next, Squeeze-and-Excitation is added as an attention module to achieve higher accuracy. Integration of predicted masks and original images into a structure-from-motion procedure is additionally taken into account. In the last step, ground control points and scale bars are considered to overcome the problem of datum rank deficiency in absolute orientation through the bundle adjustment procedure in aerial triangulation. The most probable segmented cracks are overlaid on the 3D point clouds in the global coordinate system with true scales. Our network is trained based on 8000 images and their corresponding masks, leading to 69% in Intersection over Union (IoU) index. Sub-millimetre accuracy of crack reconstruction using the proposed methodology is validated with a scale bar.