Intelligent Crack Detection and Quantification in the Concrete Bridge: A Deep Learning-Assisted Image Processing Approach

We proposed a modified concrete bridge crack detector based on a deep learning-assisted image processing approach. Data augmentation technology is introduced to extend the limited dataset. In our proposed method, the bounding box for the crack is detected by YOLOv5. Then, the image covered by the bounding box is processed by the image processing techniques. Compared with the conventional image processing-based crack detection method, the deep learning-assisted image processing approach leads to higher detection accuracy and lower computation cost. More precisely, the mask filter is employed to remove handwritten marks, and the ratio filter is adopted to eliminate speckle linear noises. When a single crack is detected by several bounding boxes, we proposed a novel fusion method to merge these bounding boxes. Furthermore, we proposed a connected component search approach based on the crack trend of the area to improve the connection accuracy. With the crack detector, the cracks that are wider than 0.15 mm can be correctly detected, quantified, and visualized. The detection absolute error of the crack width is less than 0.05 mm. Thus, we realized fast and precise detection and quantification of bridge crack based on the practical engineering dataset.

Automated summary

We propose a modified concrete bridge crack detector based on a deep learning-assisted image processing approach. The method utilizes data augmentation technology to extend the limited dataset, with YOLOv5 used to detect the bounding box for the crack. The image covered by the bounding box is then processed using image processing techniques. Compared to traditional image processing-based methods, this approach achieves higher detection accuracy and lower computation cost.