工业工程2024，Vol.27Issue(2)：27-36,66,11.DOI:10.3969/j.issn.1007-7375.230233
基于深度智能视觉的表面缺陷检测研究进展
A Review on Surface Defect Detection Based on Deep Intelligent Vision
摘要
Abstract
The exploration on surface defect detection based on deep intelligent vision plays an increasingly important role in the manufacturing industry.The importance of surface defect detection based on deep intelligent vision in modern industrial quality inspection is explained and the existing research progress is summarized in this paper.Deep intelligent vision provides high-precision and high-efficiency surface defect detection algorithms for different industrial scenarios based on the technologies of machine vision and deep learning.Surface defect detection can be divided into three categories:surface defect classification,localization,and segmentation from the perspective of detection fineness.The classification,localization,and segmentation methods are systematically reviewed,respectively,to sort out the problematic points and lines of the existing surface defect detection methods.Surface defect classification focuses on the problem of data and defective graphical features,which shows decentralized development due to its basic and easily expandable nature for application in different industrial scenarios.Surface defect localization takes the model framework,rectangular box detection mechanism,and annotation cost as the main problems,showing a research trend of pursuing lightweight and feature fusion mechanisms.Surface defect segmentation pays more attention to detailed features of an image.A multi-task framework for classification,localization,and segmentation,is studied to explore the complementarity between classification and segmentation detection.Finally,the current issues of existing surface defect detection studies are concluded and an outlook on the development trend is given.关键词
表面缺陷检测/缺陷分类/缺陷定位/缺陷分割Key words
surface defect detection/defect classification/defect localization/defect segmentation分类
管理科学引用本文复制引用
高艺平,王浩,李新宇,高亮..基于深度智能视觉的表面缺陷检测研究进展[J].工业工程,2024,27(2):27-36,66,11.基金项目
国家自然科学基金资助项目(52205523) （52205523）
