Fine Crack Detection on Bridge Surfaces Using Close-Range UAV Imaging and a Camouflaged Object Detection Network
Abstract
The detection of cracks in concrete bridges is essential for evaluating structural durability and load-carrying capacity. Although vision-based crack detection methods have been widely studied, the detection of fine cracks remains challenging. This study proposes a method for detecting fine surface cracks in bridges based on close-range unmanned aerial vehicle (UAV) photography and a camouflaged object detection network. The main contributions are as follows: 1. The feasibility of using a UAV equipped with a telephoto camera to capture fine cracks was investigated, and close-range imaging was shown to enable the acquisition of fine cracks as narrow as 0.08 mm. 2. To address the difficulty of accurately identifying cracks on concrete bridge surfaces due to stain interference and the weak texture features of fine cracks, a crack segmentation method based on a boundary-guided camouflaged object detection network was applied and validated. By improving the contextual aggregation module, the method achieved accurate identification of fine cracks under stain interference. 3. To overcome the difficulty of accurately measuring crack width when fine cracks occupy only a small number of pixels, a deep learning-based super-resolution method was applied to achieve sub-pixel-level crack width measurement. The proposed method was tested and validated on an in-service concrete bridge. The test results show that the proposed method achieved a mean absolute error of 0.7% in crack segmentation and a width measurement error of less than 0.06 mm, demonstrating its practical applicability.