针对钢箱梁内表面典型涂层病害及螺栓缺陷难以被快速感知、识别的问题，研发了具有一定普适性的大刚度多级折叠机械臂轨道机器人。基于涂层的典型病害分类、病害影响权重和涂层劣化等级评定，搭建了典型病害识别专家决策系统，并训练了多尺度感受野网络。基于计算机视觉与深度学习技术提出了一种螺栓丢失与松动缺陷鲁棒性检测方法。结果表明：钢箱梁内表面涂层病害识别准确率为 97%，分类准确率为 90. 3%；螺栓丢失缺陷识别准确率为 99. 0%，螺栓松动缺陷识别准确率为 99. 7%，螺栓缺陷检测时，透视拍摄角度不大于 40 °可减少对螺栓松紧度的误判。无人搭载平台的轨道机器人实现了对钢箱梁内表面涂层病害及螺栓缺陷的快速、高精度智能巡检。

Aiming at the problem that typical coating diseases and bolt defects on theinterior surface of steel box girders are difficult to be detected and identified rapidly，a broadlyapplicable orbital robot with large-stiffness multi-stage folding robotic arms was developed.Based on the typical disease classification of coating，disease impact weight，and coating deterioration level assessment，an expert decision-making system for typical disease identification is constructed. Additionally，a robust detection method for bolt loss and loosening defects is proposed based on computer vision and deep learning technology. Thismethod resolves the issue of inaccurate detection results caused by the poor robustness of keyfeature extraction in similar approaches. The results show that the accuracy of identifying coating defects on the interior surface of steel box girders is 97%，with a classification accuracy of 90. 3%. The accuracy of identifying lost bolts is 99. 0%，and for loose bolts，it is 99. 7%. Additionally，the detection method reduces the misjudgment of bolt looseness whenthe shooting angle is within 40 °. The rail robot of the unmanned platform achieves the fast andhigh-precision intelligent inspection of coating diseases and bolt defects on the inner surfaceof steel box girders.