Research on visual inspection methods for corrosion degree of bridge steel components
桥梁钢构件腐蚀程度的视觉检测方法研究

摘要

沿海桥梁在盐雾、振动与荷载交互环境下易出现早期锈蚀，传统人工检测效率低、精度差。为实现腐蚀程度的可视化识别与规范化分级，本文提出一种融合多平台拍摄、深度学习分割与三维指标判定的视觉检测方法。构建由无人机与磁吸爬行机器人组成的采集系统，采用Corro-U²-Net模型实现腐蚀区域的高精度掩膜提取，并引入锈蚀占比R、蚀坑深度指数D与表面形貌熵H构建等级评估体系，结合支持向量机实现规范对应。在跨湾大桥200m试验段实测中，像素级IoU达0.87，等级一致率达91%，较现有方法提升显著，并在养护费用与决策效率上体现出良好工程适应性。

Abstract

Coastal Bridges are prone to early rusting under the interaction of salt spray, vibration and load. Traditional manual detection is inefficient and inaccurate. To achieve visual identification and standardized classification of corrosion degrees, this paper proposes a visual inspection method that integrates multi-platform shooting, deep learning segmentation and three-dimensional index determination. A acquisition system composed of unmanned aerial vehicles (UAVs) and magnetic crawling robots was constructed. The Corro-U²-Net model was adopted to achieve high-precision mask extraction in the corrosion area. The rust proportion R, crater depth index D and surface topography entropy H were introduced to build a grade evaluation system, and the support vector machine was combined to achieve specification correspondence. In the actual measurement of the 200-meter test section of the Cross-Bay Bridge, the pixel-level IoU reached 0.87, and the grade consistency rate was 91%, which was significantly improved compared with the existing methods. Moreover, it demonstrated good engineering adaptability in terms of maintenance costs and decision-making efficiency.