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%.

中文翻译：

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使用环形攀爬机器人自动评估高层桥梁桥墩的裂缝

本文提出了一种使用环型攀爬机器人的高层桥梁墩基深度学习自动裂缝评估技术。首先，开发了由多个视觉摄像机，攀爬机器人和控制计算机组成的环形攀爬机器人系统。通过在近距离扫描条件下沿着目标桥墩在空间上移动攀爬机器人系统，可以连续获取高质量的原始视觉图像。然后，通过基于特征控制的图像拼接，基于深度学习的语义分割以及基于欧氏距离变换的裂缝量化算法来处理原始视觉图像。最后，可以自动建立目标桥墩的感兴趣区域（ROI）上的数字裂缝图。所提出的技术已通过现场实验验证测试数据来自韩国的张鸭桥。测试结果表明，所提出的技术成功地评估了桥墩整个ROI上的裂纹，其精度为90.92％，召回率为97.47％。