Automated crack evaluation of a high-rise bridge pier using a ring-type climbing robot

This article proposes a deep learning-based automated crack evaluation technique for a high-rise bridge pier using a ring-type climbing robot. First, a ring-type climbing robot system composed of multiple vision cameras, climbing robot, and control computer is developed. By spatially moving the climbing robot system along a target bridge pier with close-up scanning condition, high-quality raw vision images are continuously obtained. The raw vision images are then processed through feature control-based image stitching, deep learning-based semantic segmentation, and Euclidean distance transform–based crack quantification algorithms. Finally, a digital crack map on the region of interest (ROI) of the target bridge pier can be automatically established. The proposed technique is experimentally validated using in situ test data obtained from Jang–Duck bridge in South Korea. The test results reveal that the proposed technique successfully evaluates cracks on the entire ROI of the bridge pier with precision of 90.92% and recall of 97.47%.