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Summary: A large crack detection dataset of 2446 manually labeled images is established to cover a wide range of noise and to evaluate the performance of end‐to‐end deep convolutional networks in detecting cracking. Five state‐of‐the‐art end‐to‐end deep computer vision architectures for semantic segmentation are trained and evaluated, including Fully Convolutional Network (FCN), Global Convolutional Network (GCN), Pyramid Scene Parsing Network (PSPNet), UPerNet, and DeepLabv3+. For the backbones, the VGG, ResNet, and DenseNet are adopted. Based on the comparison of test set metrics, DeepLabv3+ with the ResNet101 backbone achieved the highest IoU of 0.6298, the highest recall of 0.6834, and the highest F1 score of 0.7732. The influence of database choice and image noise on crack detection performance is reported. Based on the comparison of predicted images, UperNet with ResNet101 backbone shows the highest performance for images with shadings, while DeepLabv3+ with ResNet101 backbone shows the best performance for images with blemishes. The research outcome can provide reference for the application of fast and accurate detection of cracks in civil engineering. [ABSTRACT FROM AUTHOR]

For digital-image-based bridge inspection tasks, images captured by camera-carrying unmanned aircraft vehicles (UAVs) usually contain both the region of interest (ROI) and the background. However, accurately detecting cracks in concrete surface images containing background information is challenging. To improve UAV-based bridge inspection, an image extraction and crack detection methodology is presented in this paper. First, a deep-learning-based semantic segmentation network RandLA-BridgeNet for large-scale bridge point clouds, which can facilitate 3D ROI extraction, is trained and tested. Second, an image ROI extraction method based on 3D-to-2D projection is presented to generate images containing only the ROI. Finally, a data-driven deep learning convolutional neural network (CNN) called the grid-based classification and box-based detection fusion model (GCBD) is utilized to identify cracks in the processed images. An experiment is conducted on highway bridge images to validate the presented methodology. The overall semantic segmentation and image ROI extraction accuracies are 97.0% and 98.9%, respectively. After ROI extraction, 47.9% of the grid cells, which represent background misrecognition, are filtered, greatly improving the crack identification accuracy. • A deep-learning-based bridge point cloud semantic segmentation network RandLA-BridgeNet is developed to extract 3D ROI. • An image ROI extraction method based on 3D-to-2D projection is proposed to generate images containing only the ROI. • A grid-based classification and box-based detection fusion model (GCBD) is presented to identify cracks in bridge images. • A UAV-based bridge inspection experiment is conducted on a highway bridge to validate the proposed methodology. • The proposed ROI extraction method improves the crack detection accuracy using images with background information. [ABSTRACT FROM AUTHOR]

The inspection of bridges is increasingly dependent on advanced equipment and algorithms like digital cameras and SfM (Structure from Motion). However, many existing SfM-based bridge inspection methods lack efficiency due to lengthy 3D reconstruction computation times, and digital image resolution often falls short in detecting fine cracks and calculating their widths, mainly influenced by the acquisition equipment. This paper describes a fast and accurate crack assessment method that leverages multi-sensor fusion SLAM (Simultaneous Localization and Mapping) and image super-resolution. Through multi-sensor fusion SLAM, textured point clouds of the bridge structure can be obtained directly, significantly improving efficiency. Furthermore, deep learning-based image super-resolution enhances the precision of crack width calculation. Field tests demonstrate the effectiveness of the proposed methods, showcasing a 94% reduction in scene reconstruction time and a 16% improvement in crack width calculation accuracy. • Multi-sensor fusion SLAM is applied in bridge inspection for the first time which greatly improves the inspection efficiency. • A deep learning-based crack image super-resolution model is trained to enhance the accuracy of width calculation. • The performance of both SLAM and SfM methods for scene reconstruction are meticulously compared. • A quantitative study is conducted to assess the impact of image super-resolution on the accuracy of crack width calculation. [ABSTRACT FROM AUTHOR]