Visual Recognition Method for Abnormal States of Dense Bolts for Steel Bridges

被引：0
作者：
Wang B. [1 ,2 ]
Ou B. [3 ]
Zhao W. [1 ,2 ]
Tan Z. [4 ]
Qin S. [4 ]
机构：
[1] Structural Health Monitoring and Control Key Laboratory of Hebei Province, Shijiazhuang Tiedao University, Hebei, Shijiazhuang
[2] State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Hebei, Shijiazhuang
[3] School of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Hebei, Shijiazhuang
[4] National Engineering Research Center for Digital Construction and Evaluation Technology of Urban Rail Transit, Tianjin
来源：
Zhongguo Tiedao Kexue/China Railway Science | 2023年 / 44卷 / 05期
关键词：
Bolts; Clustering density analysis; Falling off; Loosening; Projection analysis; Steel bridges; Visual recognition;
D O I：
10.3969/j.issn.1001-4632.2023.05.09
中图分类号：
学科分类号：
摘要：
In view of the issue that traditional machine vision methods cannot identify abnormal bolts well in the images taken from different shooting angles and distances, a vision recognition method for abnormal states of dense bolts based on regional abnormal point analysis is proposed according to the visual characteristics of the dense bolt areas in steel bridges. Firstly, this method extracts and compares the grayscale of the blue and red channels in the image to complete the color segmentation of the beam body. It then selects the Canny operator to extract the inner edge of the beam body area and uses the Hough line recognition method to eliminate clutter. Secondly, based on the cluster distribution characteristics of dense bolts, the bolt cluster area is located by clustering density analysis, and based on the parallel grid distribution characteristics of dense bolt positions, the single bolt area is located by projection analysis. Finally, according to the shadow features of each bolt, Chebyshev's inequality is used to quickly determine the bolt states and complete the recognition of abnormal bolts. A gusset plate model of the steel bridge is created, and different images of bolt loosening or falling off are collected for method testing. The results show that the method is highly applicable to image shooting angles and distances, and the recognition ability of bolt falling off is greater than that of bolt loosening. In different scenarios, the average intersection ratio of a single bolt location is greater than 0. 75. The accuracy and recall rates of bolt falling off and loosening recognition are above 0. 89 and 0. 85, respectively. © 2023 Chinese Academy of Railway Sciences. All rights reserved.
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页码：81 / 93
页数：12
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