北京交通大学学报2023，Vol.47Issue(5)：16-24,9.DOI:10.11860/j.issn.1673-0291.20230067
基于图像处理的桥梁橡胶支座剪切病害检测方法
Image processing-based method for detecting shear damage in bridge rubber bearings
摘要
Abstract
Addressing the complexities presented by the beam and bridge plate rubber bearings,in-cluding their challenging accessibility because of deep location and the intricate measurement of shear deformation angles,this study introduces an automated method for shear angle calculation and shear damage assessment based on image analysis.Firstly,a U-Net network integrated with depth-wise separable convolution and an Multi-scale Attention Module(MAM)is employed to identify and segment the bearings within the images.Secondly,the binary image of the segmented bearings is used for extracting bearing contour lines through a simplified Alpha Shapes algorithm.Then the con-vex packet detection is performed to extract the convex packet points and their respective coordi-nates.Finally,the least-squares method is utilized to fit the convex packet points into straight lines,quantifying the degree of shear damage through the calculation of the shear angle between these lines.The research results show that the improved U-Net model for bearing segmentation demon-strates F1 scores and Intersection over Union(IoU)exceeding 95%.In a bridge inspection con-ducted in Tianjin,this paper's method is utilized to compare angle calculations from camera-captured bearing images with manual measurements.The maximum error between the two is re-corded at a mere 1.3 °,and shear damage level classification produces consistent results.This paper's method paves the way for non-contact,automatic shear damage detection in rubber bearings,pro-viding valuable insights for practical engineering applications.关键词
桥梁工程/板式橡胶支座/剪切病害/图像处理/U-NetKey words
bridge engineering/plate rubber bearing/shear damage/image processing/U-Net分类
交通运输引用本文复制引用
梁栋,张少杰,周印霄,王莲香,张强,刘跃飞..基于图像处理的桥梁橡胶支座剪切病害检测方法[J].北京交通大学学报,2023,47(5):16-24,9.基金项目
国家自然科学基金(51978236) （51978236）
天津市交通运输委员会科技发展项目计划(2023-50) National Natural Science Foundation of China(51978236) （2023-50）
Science and Technology Development Project of Tianjin Trans-portation Commission(2023-50) （2023-50）
