Search

The rapid and accurate detection of road cracks is of great significance for road health monitoring, but currently, this work is mainly completed through manual site surveys. Low-altitude UAV remote sensing can provide images with a centimeter-level or even subcentimeter-level ground resolution, which provides a new, efficient, and economical approach for rapid crack detection. Nevertheless, crack detection networks face challenges such as edge blurring and misidentification due to the heterogeneity of road cracks and the complexity of the background. To address these issues, we proposed a real-time edge reconstruction crack detection network (ERNet) that adopted multi-level information aggregation to reconstruct crack edges and improve the accuracy of segmentation between the target and the background. To capture global dependencies across spatial and channel levels, we proposed an efficient bilateral decomposed convolutional attention module (BDAM) that combined depth-separable convolution and dilated convolution to capture global dependencies across the spatial and channel levels. To enhance the accuracy of crack detection, we used a coordinate-based fusion module that integrated spatial, semantic, and edge reconstruction information. In addition, we proposed an automatic measurement of crack information for extracting the crack trunk and its corresponding length and width. The experimental results demonstrated that our network achieved the best balance between accuracy and inference speed compared to six established models. [ABSTRACT FROM AUTHOR]

Machine learning and deep neural network developments have evolved image classification. This study offers a new neural network design with segmentation and localization, different crack types, categorization accuracy, and efficiency. In the proposed work, an advanced and innovative Spliced Multimodal Residual18 Neural Network (SMR18-NN) Model is presented that departs from previous models. For data authentication, augmentation is done by proposing the new and effective model known as an Augmented Minority Over-sampling Technique (AMOST). The SMR18 NN model combines the well-established ResNet18 framework with the Faster RCNN architecture, incorporating parts from the modified Fast RCNN and the Region Proposal Network. This work aims to revolutionize crack-type recognition and categorization in images. A Support Vector Machine strategically improves the network’s data classification. A modified ResNet18 model is ultimately implemented and compared with the proposed innovative SMR18-NN model. Both network’s parameters, such as epochs, number of iterations, etc., were kept the same for fair evaluation. Innovative frameworks and properly selected benchmark datasets supported this. The empirical results of this comparison study are convincing. ResNet18 training and testing accuracy was 90.60% and 85.20%, respectively. The SMR18-NN outperformed these results with 96.20 training and 92.00% testing accuracy. The experiment concluded with SMR18-NN accurately detecting image features, proving its superiority in image classification.

Existing studies often lack a systematic solution for an Unmanned Aerial Vehicles (UAV) inspection system, which hinders their widespread application in crack detection. To enhance its substantial practicality, this study proposes a formal and systematic framework for UAV inspection systems, specifically designed for automatic crack detection and pavement distress evaluation. The framework integrates UAV data acquisition, deep-learning-based crack identification, and road damage assessment in a comprehensive and orderly manner. Firstly, a flight control strategy is presented, and road crack data are collected using DJI Mini 2 UAV imagery, establishing high-quality UAV crack image datasets with ground truth information. Secondly, a validation and comparison study is conducted to enhance the automatic crack detection capability and provide an appropriate deployment scheme for UAV inspection systems. This study develops automatic crack detection models based on mainstream deep learning algorithms (namely, Faster-RCNN, YOLOv5s, YOLOv7-tiny, and YOLOv8s) in urban road scenarios. The results demonstrate that the Faster-RCNN algorithm achieves the highest accuracy and is suitable for the online data collection of UAV and offline inspection at work stations. Meanwhile, the YOLO models, while slightly lower in accuracy, are the fastest algorithms and are suitable for the lightweight deployment of UAV with online collection and real-time inspection. Quantitative measurement methods for road cracks are presented to assess road damage, which will enhance the application of UAV inspection systems and provide factual evidence for the maintenance decisions made by road authorities. [ABSTRACT FROM AUTHOR]

The rapid and accurate detection of road cracks is of great significance for road health monitoring, but currently, this work is mainly completed through manual site surveys. Low-altitude UAV remote sensing can provide images with a centimeter-level or even subcentimeter-level ground resolution, which provides a new, efficient, and economical approach for rapid crack detection. Nevertheless, crack detection networks face challenges such as edge blurring and misidentification due to the heterogeneity of road cracks and the complexity of the background. To address these issues, we proposed a real-time edge reconstruction crack detection network (ERNet) that adopted multi-level information aggregation to reconstruct crack edges and improve the accuracy of segmentation between the target and the background. To capture global dependencies across spatial and channel levels, we proposed an efficient bilateral decomposed convolutional attention module (BDAM) that combined depth-separable convolution and dilated convolution to capture global dependencies across the spatial and channel levels. To enhance the accuracy of crack detection, we used a coordinate-based fusion module that integrated spatial, semantic, and edge reconstruction information. In addition, we proposed an automatic measurement of crack information for extracting the crack trunk and its corresponding length and width. The experimental results demonstrated that our network achieved the best balance between accuracy and inference speed compared to six established models.

This paper proposes a road crack detection algorithm based on an improved SparseInst network, called the SparseInst-CDSM algorithm, aimed at solving the problems of low recognition accuracy and poor real-time detection of existing algorithms. The algorithm introduces the CBAM module, DCNv2 convolution, SPM strip pooling module, MPM mixed pooling module, etc., effectively improving the integrity and accuracy of crack recognition. At the same time, the central axis skeleton of the crack is extracted using the central axis method, and the length and maximum width of the crack are calculated. In the experimental comparison under the self-built crack dataset, SparseInst-CDSM has an accuracy of 93.66%, a precision of 67.35%, a recall of 66.72%, and an IoU of 84.74%, all higher than mainstream segmentation models such as Mask-RCNN and SOLO that were compared, reflecting the superiority of the algorithm proposed in this paper. The comparison results of actual measurements show that the algorithm error is within 10%, indicating that it has high effectiveness and practicality. [ABSTRACT FROM AUTHOR]

Road cracks, which are a common hazard in pavements throughout the life cycle of a road, can degrade the performance of the road, shorten its service life, and endanger the safety of vehicles. Traditional vision machine detection methods can detect road crack details but suffer from poor stability and generalization ability, whereas semantic segmentation detection, although more stable, cannot track fine road crack information. To combine the advantages of both methods and improve the accuracy of road crack detection, a novel feature fusion road crack detection method is proposed in this study. First, the bilateral filter and four-way Sobel operator are introduced into the Canny algorithm to enhance the noise reduction effect and extract edge features more effectively. Second, the dynamic threshold is generated adaptively by the gradient information after non-maximum suppression. Subsequently, the detection map is morphologically processed, the connected areas are ranked, and the bilateral filter parameters are adjusted based on the detection results. The Canny road crack detection map is then extracted by the convolutional feature extraction module, fused with the low feature layer in the DeepLabV3+ detection network, and finally stitched with the high feature layer; the resulting map is obtained after convolutional feature extraction. The method was validated on the publicly available complex road crack dataset CRACK500; the experimental results showed that feature fusion outperformed the adaptive Canny, DeepLabV3+, Unet, PSPnet, and ICnet algorithms by more than 6.5% on the Mean Intersection over Union(MIoU) and also in Mean Absolute Error(MAE) by effectively combining crack features and improving the detection accuracy.

Existing studies often lack a systematic solution for an Unmanned Aerial Vehicles (UAV) inspection system, which hinders their widespread application in crack detection. To enhance its substantial practicality, this study proposes a formal and systematic framework for UAV inspection systems, specifically designed for automatic crack detection and pavement distress evaluation. The framework integrates UAV data acquisition, deep-learning-based crack identification, and road damage assessment in a comprehensive and orderly manner. Firstly, a flight control strategy is presented, and road crack data are collected using DJI Mini 2 UAV imagery, establishing high-quality UAV crack image datasets with ground truth information. Secondly, a validation and comparison study is conducted to enhance the automatic crack detection capability and provide an appropriate deployment scheme for UAV inspection systems. This study develops automatic crack detection models based on mainstream deep learning algorithms (namely, Faster-RCNN, YOLOv5s, YOLOv7-tiny, and YOLOv8s) in urban road scenarios. The results demonstrate that the Faster-RCNN algorithm achieves the highest accuracy and is suitable for the online data collection of UAV and offline inspection at work stations. Meanwhile, the YOLO models, while slightly lower in accuracy, are the fastest algorithms and are suitable for the lightweight deployment of UAV with online collection and real-time inspection. Quantitative measurement methods for road cracks are presented to assess road damage, which will enhance the application of UAV inspection systems and provide factual evidence for the maintenance decisions made by road authorities.

This paper proposes a road crack detection algorithm based on an improved SparseInst network, called the SparseInst-CDSM algorithm, aimed at solving the problems of low recognition accuracy and poor real-time detection of existing algorithms. The algorithm introduces the CBAM module, DCNv2 convolution, SPM strip pooling module, MPM mixed pooling module, etc., effectively improving the integrity and accuracy of crack recognition. At the same time, the central axis skeleton of the crack is extracted using the central axis method, and the length and maximum width of the crack are calculated. In the experimental comparison under the self-built crack dataset, SparseInst-CDSM has an accuracy of 93.66%, a precision of 67.35%, a recall of 66.72%, and an IoU of 84.74%, all higher than mainstream segmentation models such as Mask-RCNN and SOLO that were compared, reflecting the superiority of the algorithm proposed in this paper. The comparison results of actual measurements show that the algorithm error is within 10%, indicating that it has high effectiveness and practicality.

Road cracks are caused due to the abundant usage of the roads, heavy traffic, and increased prerequisite of transportation. So road maintenance is an important aspect of such a huge number of vehicles on the roads to have safety measures and continuity. Besides the traffic, bad weather is also contributing its part in creating road cracks. In the proposed research automatic road cracks have been detected, it looks simple apparently but the intensity, and complexity of the background make it a challenging task. In this challenging task, the contrast of the processed image, complexity of different kinds of crack recognition, assembly of proper database images, elapsed time, and approximate classification, etc., are processed to get the optimum results. Deep learning has multiple neural networks among them few networks are used like AlexNet, ResNet18, and SqueezeNet for the data recognition mainly for the huge database having the optimum results in minimum throughput. In the accomplished research 4333 images with eight diverse road cracks classes are used. The classified images are processed by utilizing three different networks through a supervised dataset. Dataset is built by mixing the university resources-collected images and some online datasets. The best expected result is gained after proper training and tested along with classification. In this experiment, the training and testing images were kept the same in epoch and iteration. But the ResNet18 was superlative with an accuracy of 85.20%. [ABSTRACT FROM AUTHOR]

Road cracks are one of the external manifestations of safety hazards in transportation. At present, the detection and segmentation of road cracks is still an intensively researched issue. With the development of image segmentation technology of the convolutional neural network, the identification of road cracks has also ushered in new opportunities. However, the traditional road crack segmentation method has these three problems: 1. It is susceptible to the influence of complex background noise information. 2. Road cracks usually appear in irregular shapes, which increases the difficulty of model segmentation. 3. The cracks appear discontinuous in the segmentation results. Aiming at these problems, a network segmentation model of HC-Unet++ road crack detection is proposed in this paper. In this network model, a deep parallel feature fusion module is first proposed, one which can effectively detect various irregular shape cracks. Secondly, the SEnet attention mechanism is used to eliminate complex backgrounds to correctly extract crack information. Finally, the Blurpool pooling operation is used to replace the original maximum pooling in order to solve the crack discontinuity of the segmentation results. Through the comparison with some advanced network models, it is found that the HC-Unet++ network model is more precise for the segmentation of road cracks. The experimental results show that the method proposed in this paper has achieved 76.32% mIOU, 82.39% mPA, 85.51% mPrecision, 70.26% dice and Hd95 of 5.05 on the self-made 1040 road crack dataset. Compared with the advanced network model, the HC-Unet++ network model has stronger generalization ability and higher segmentation accuracy, which is more suitable for the segmentation detection of road cracks. Therefore, the HC-Unet++ network model proposed in this paper plays an important role in road maintenance and traffic safety.

Road deteriorations threaten people ‘safety as they are considered one of the major causes of road accidents leading to significant human and material losses. Automatic detection of road deteriorations can help massively in avoiding the tragic consequences. However, this task represents a real challenge. This work describes an attempt to tackle this issue by establishing a crack notification and road condition monitoring system. The proposed system will help drivers to avoid dangerous cracks and enable authorities to supervise and manage the road surface. In order to automatically detect and classify the road surface images, a convolutional neural network (CNN) is used, as this model has proven to be more suitable for this task than traditional methods due to its ability to extract features implicitly. The novelty in this paper consists on finding the optimal CNN model architecture for crack detection and classification into 10 distinguished classes. The proposed CNN model is trained and tested using historical 3D pavement images and can perform the classification with a high accuracy (above 95%). To evaluate and supervise each road segment condition, a novel method for severity indexes computation that uses the resulting classes is suggested as well as the generation of a weighted road graph in which the edge weights evolve according to the severity indexes. All data processing tasks are performed over a Hadoop-based framework using HBase and MapReduce. [ABSTRACT FROM AUTHOR]

Recent convolutional neural networks have made significant advancements in the detection of road cracks. However, the lack of accurate crack training data reduces the generalisation ability of the deep model. In this Letter, a semi‐automatic pavement crack labelling algorithm is proposed to solve the problem of insufficient training data. First, the modified C–V model is used to obtain the preliminary segmentation results. Second, the direction of the initial segmentation area is calculated by the ellipse fitting method, and the preliminary segmentation results are used as samples for accurate labelling. Finally, a multi‐scale feature extraction module is proposed for learning rich deep convolutional features, which allows the acquired crack features under a complex background to be more discriminant. The experimental results were compared with the manual marking method, and this method can achieve accurate marking of crack images with a low amount of interaction, thereby significantly reducing the cost of ground‐truth making. The results of the validation and comparison experiments on test data sets indicate that the proposed method can not only effectively identify cracks, but also overcome the interference of many factors in the environment. [ABSTRACT FROM AUTHOR]