Journal of Guangxi Normal University(Natural Science Edition) ›› 2020, Vol. 38 ›› Issue (6): 21-31.doi: 10.16088/j.issn.1001-6600.2020.06.003

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A Method for Detecting Metal Surface Defects Based on Photometric Stereo and Series Expansion Methods

ZHU Yongjian*, LUO Jian, QIN Yunbai, QIN Guofeng, TANG Chuliu   

  1. College of Electronic Engineering, Guangxi Normal University, Guilin Guangxi 541004, China
  • Received:2020-02-29 Published:2020-11-30

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Abstract: In the detection of metal surface defects with rough textures, the online machine vision inspection system encounters the problems of error detection, missing detection. In addition, photometric solids have problems such as distortion and warpage of reconstructed curved surfaces when reconstructing the three-dimensional topography of metal surfaces. To address these problems, a metal surface defect detection method based on photometric solid and series expansion is proposed. Firstly, this method uses the photometric stereo principle to reconstruct the metal surface in three dimensions. Secondly, uses the series expansion method to fit the reconstructed surface. Then, the fitted surface is converted into point cloud data. Finally, the point cloud data is processed to complete defect detection.Experimental results show that this method solves the problem of distortion and warpage of reconstructed surfaces, and at the same time, improves the ability to detect defects on metal surfaces. Compared with the method without surface fitting, the defect detection rate of this method has increased by more than 4 percentage points.

Key words: photometric stereo, series expansion, point cloud data, metal surface defects, detection

CLC Number: 

  • TP391
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