基于机器视觉的表面缺陷检测技术研究
Research on Surface Defect Detection Technology Based on Machine Vision
陈烨强1
作者信息
- 1. 闽江师范高等专科学校,福州 350108
- 折叠
摘要
自动化生产过程中产品表面的缺陷检测至关重要,传统的人工缺陷检测方法存在速度慢、效率低、劳动强度高、主观性强等问题,难以适应当前智能制造的发展要求.基于此,阐述基于机器视觉的表面缺陷检测技术流程,先通过工业相机对产品进行拍照成像,然后结合生产环境对图像进行去噪处理,最后提出一种基于区域聚类的显著性检测算法进行图像像素分割、特征提取、MeanShift聚类、更新新特征以及循环结果,提取缺陷分割图.
Abstract
The defect detection of product surface is very important in the automatic production process.The traditional artificial defect detection method has some problems such as slow speed,low efficiency,high labor intensity and strong subjectivity,which is difficult to adapt to the development requirements of current intelligent manufacturing.Based on this,the surface defect detection technology process based on machine vision is described.Firstly,the product is photographed and imitated by industrial cameras,and then the image is de-noised in combination with the production environment.Finally,a significance detection algorithm based on regional clustering is proposed for image pixel segmentation,feature extraction,MeanShift clustering,updating new features and cyclic results.Extract defect segmentation diagram.
关键词
表面缺陷检测/机器视觉/像素分割Key words
surface defect detection/machine vision/pixel segmentation引用本文复制引用
出版年
2024
NETL
NSTL
万方数据