双光源下光学镜片表面疵病视觉检测方法  

Visual Detection Method of Optical Lens Surface Defect Under Dual Light Sources

作　　者：徐顺琴 杨利红[1] 付秦月 陈千喜 李星元 葛航 Xu Shunqin;Yang Lihong;Fu Qinyue;Chen Qianxi;Li Xingyuan;Ge Hang(College of Photoelectric Engineering,Xi'an Technological University,Xi'an,710021,Shaanaci,China)

机构地区：[1]西安工业大学光电工程学院,陕西西安710021

出　　处：《激光与光电子学进展》2024年第10期237-243,共7页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(62071359)。

摘　　要：针对机器视觉检测光学镜片表面疵病时,在单一的照明环境下疵病图像对比度低,检测方法疵病识别率低等问题,提出了一种双光源下光学镜片表面疵病视觉检测方法。根据散射成像原理,在前照光和背照光两种不同的照明方式下,使用图像传感器得到含有疵病的被测光学镜片图像;再将多幅图像通过图像融合算法融合为一幅图像;最后,利用识别算法获得光学镜片表面的疵病尺寸信息。对两种不同的疵病(划痕、麻点)进行检测,将本系统的测试结果与ZYGO干涉仪的处理结果进行对比,结果表明,所提方法测量的麻点误差不超过2.7%,划痕误差不超过0.8%,检测效率比干涉仪提高了98.24%,缩短了检测时间。与单一照明环境下的检测方法和人工检测相比,所提方法对疵病的识别准确率与精度更高。Aiming at the problems of low contrast of the optical lens image and low recognition rate of optical lens surface defects under single illumination when detecting optical lens surface defects by machine vision,a visual detection method of optical lens surface defects under dual light sources is proposed.According to the scattering imaging principle,the optical lens images containing defects are obtained by using the image sensor under two different illumination modes,the forward light and the backlight,and then the images are fused into one image by the image fusion algorithm.Finally,the defect size information of optical lens surface is obtained by using the recognition algorithm.Two different defects(scratch,pitting)are detected,and the test results of this system are compared with the processing results of the ZYGO interferometer,and the comparative results show that the pitting error and the scratch error of proposed method are less than 2.7%and 0.8%,respectively,the detection efficiency is inproved by 98.24%compared with the interferometer,and the detection time is shortened.Compared with the detection method under single illumination and manual detection,the identification rate and accuracy of defects detected by the proposed method is higher.

关 键 词：疵病检测 双光源 机器视觉 散射成像 图像融合 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]