Surface Inspection of Polished Rail Welds Based on Photometric Stereo

doi:10.3901/JME.2024.24.011

Abstract

Abstract: A multi-surface information acquisition system for rails is designed to realize the image sequence capture of photometric stereo under multiple illumination directions. Normal estimation of grinding surfaces is implemented based on an excellent photometric stereo method. By analyzing the characteristics of polished surface normals and defect features, an adaptive polishing region extraction method is first proposed to eliminate the interference of the background areas. Secondly, a horizontal pattern defect detection algorithm based on two-stage and rotating rectangle extraction and a height mutation defect detection algorithm based on dual-threshold segmentation of grayscale histogram is proposed, which overcomes the noise and pseudo defect interference and effectively solves the problem of difficulty in defect feature extraction and localization. Experimental results demonstrate the superiority of the proposed polished surface defect detection algorithm over existing deep learning-based defect detection algorithms. The detection accuracy of the horizontal pattern reaches 94.4%, with a low omission rate of 0.84%. The accuracy of height mutation detection is 92.64%, with a low omission rate rate of 1.56%. The overall operation efficiency of the system is 23.784 s per rail welding surface, which meets the actual production requirements.

Key words: photometric stereo, defect detection, rail weld grinding surface, normal features, feature extraction

CLC Number:

TP391

WEI Xiaoyao, LIU Wenyuan, WANG Lingyu, DING Binjie, CAO Yanlong, YANG Jiangxin, HUANG Fang. Surface Inspection of Polished Rail Welds Based on Photometric Stereo[J]. Journal of Mechanical Engineering, 2024, 60(24): 11-24.

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