吉林大学学报(信息科学版) ›› 2023, Vol. 41 ›› Issue (4): 717-725.

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基于机器视觉的 O 型密封圈外观缺陷检测 

王 凯, 刘 伟, 查长军   

  1. 合肥学院 先进制造工程学院, 合肥 230601
  • 收稿日期:2022-09-16 出版日期:2023-08-16 发布日期:2023-08-17
  • 通讯作者: 查长军(1980— ), 男, 安徽怀宁人, 合肥学院高级实验师, 博士, 主要从事 压缩感知与模式识别研究, (Tel)86-13856908108(E-mail)zhachangjun@ hfuu. edu. cn。
  • 作者简介:王凯(1996— ), 男, 河南永城人, 合肥学院硕士研究生, 主要从事图像处理、 机器视觉研究, ( Tel) 86-15090628430 (E-mail)296624947@ qq. com;
  • 基金资助:
    安徽省科技重大专项基金资助项目(202003a06020022)

Machine Vision-Based Appearance Defect Detection of O-Ring Seals 

WANG Kai, LIU Wei, ZHA Changjun   

  1. School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, China
  • Received:2022-09-16 Online:2023-08-16 Published:2023-08-17

摘要: 针对 O 型密封圈表面细微缺陷检测困难的问题, 提出了一种基于6 光度立体法和图像综合特征分析的密 封圈缺陷检测方法。 首先采集 6 个不同光源角度的图片, 利用光度立体法重构表面梯度图和反射率图。 然后 将表面梯度图先转化为平均曲率和高斯曲率图像, 再转化为灰度图并使用固定阈值分割出缺陷区域。 将反射 率图经高斯滤波后, 采用局部的均值和方差阈值分割缺陷区域。 最后, 对得到的缺陷区域连通域特征分析并准 确选择出缺陷。 实验测试结果表明, 该方法对密封圈表面存在熔痕、 凹凸、 流痕等细微缺陷有较好的效果。 在 所设计的密封圈质量检测系统的应用中, 检测准确度大于 98. 4% , 能解决目前工业中存在的密封圈缺陷检测 识别率不高的问题。

关键词: 机器视觉, 密封圈, 光度立体法, 特征分析, 外观缺陷检测 

Abstract:  Aiming at the difficulty of detecting subtle defects on O-ring surface, we present a method of detecting defects on O-ring surface based on six photometric stereoscopic method and image comprehensive feature analysis. First, the images of six different light source angles are collected, and the surface gradient map and reflectance map are reconstructed by photometric stereoscopic method. The surface gradient image is first converted into the average curvature and Gaussian curvature image, and then converted into the gray-scale image. The defect region is segmented using a fixed threshold. After the reflectivity map is filtered by Gauss, the local mean and variance thresholds are used to segment the defect area. Finally, the defects are accurately selected by analyzing the connected domain characteristics of the obtained defect regions. The experimental test results show that it has a good effect on the subtle defects such as weld marks, concave-convex and flow marks on the surface of the seal ring. In the application of the designed seal ring quality detection system, the detection accuracy is more than 98. 4% , which can solve the problem of low recognition rate of the current industrial sealing ring defect detection.

Key words: machine vision, sealing ring, photometric stereoscopic method, characteristics analysis, appearance defect detection

中图分类号: 

  • TP391. 41