基于机器视觉的O型密封圈外观缺陷检测  

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

作　　者：王凯[1] 刘伟[1] 查长军[1] WANG Kai;LIU Wei;ZHA Changjun(School of Advanced Manufacturing Engineering,Hefei University,Hefei 230601,China)

机构地区：[1]合肥学院先进制造工程学院,合肥230601

出　　处：《吉林大学学报（信息科学版）》2023年第4期717-725,共9页Journal of Jilin University（Information Science Edition）

基　　金：安徽省科技重大专项基金资助项目(202003a06020022)。

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。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.

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 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]