光学精密工程2025，Vol.33Issue(20)：3203-3213,11.DOI:10.37188/OPE.20253320.3203
机器视觉测量明暗场复合照明系统优化设计
Optimization design of combined brightfield and darkfield illumination system for machine vision measurement
摘要
Abstract
In order to improve the image quality of machine vision inspection systems,this paper studies the problem of refining the arrangement of light sources for composite illumination in bright-dark fields.First,the illuminance distribution model of the bright and dark field composite lighting system was estab-lished based on Lambert radiation characteristics and linear superposition principle for the first time and then the optimization objective function including the coefficient of variation and the minimum maximum ra-tio was constructed.Next,an improved simulated annealing-particle swarm algorithm was used to solve for the optimal layout parameters.An experimental platform was constructed and the illuminance distribu-tion in the target area was measured.Then,to evaluate the effectiveness of the refined scheme,images of the workpieces in the target area were collected,and the illuminance uniformity was calculated and com-pared with experimental measurements.The measured optimal illuminance uniformity reached 0.9219,showing a relative error of 2.59%compared to the AE-SAPSO optimized result.The maximum relative error between the measured illuminance uniformity of the workpiece surface and the experimental results is 3.42%.Finally,the performance of the proposed lighting scheme was analyzed by comparing defect visi-bility under three illumination modes from the perspectives of binarization quality and illuminance uniformi-ty.The results demonstrate that the proposed light source layout method effectively enhances illumination uniformity in the target area.关键词
机器视觉/明暗场复合照明/照度均匀度/光照设计Key words
machine vision/composite illumination in bright-dark fields/illuminance uniformity/lighting design分类
机械制造引用本文复制引用
郝飞,顾志鹏,关鸿耀,高海涛,孟超..机器视觉测量明暗场复合照明系统优化设计[J].光学精密工程,2025,33(20):3203-3213,11.基金项目
国家自然科学基金项目(No.51705238) （No.51705238）
江苏高校自然科学研究重大项目(No.23KJA460009) （No.23KJA460009）
