表面缺陷检测的机器视觉技术研究进展

Research progress of surface defect detection based on machine vision technology

物体表面缺陷检测技术是工业质检领域的一项重大课题,对工业生产有着重要的意义。针对近些年基于机器视觉的表面缺陷检测技术进行梳理总结。首先,列举了几种缺陷检测在工业领域的应用场景;其次从特征提取和分类算法的角度简要阐述了传统的机器视觉方法;重点探讨了缺陷检测中常用的经典神经网络结构和缺陷检测算法的最新发展,并介绍了两种常用的缺陷检测算法优化方式;最后,分析了缺陷检测领域面临的三大挑战:实时性问题、小样本问题和小目标问题,目的是为工业表面缺陷检测的研究提供有益的参考和脉络梳理。

Object surface defect detection technology is an important subject in the field of industrial quality inspection,which is of great significance to industrial production.This paper mainly summarized the surface defect detection techniques based on machine vision in recent years.Firstly,it listed several application scenarios of defect detection in the industry.Secondly,from the perspective of feature extraction and classification algorithms,it expounded on traditional machine vision methods.Then,this paper focused on the classical neural network structure and the latest development of defect detection algorithms and introduced two commonly used optimization methods of the defect detection algorithm.Finally,it analyzed three major challenges in the field of defect detection:real-time problems,small sample problems and small target problems.The purpose was to provide useful references and context for the research of industrial surface defect detection.