基于深度智能视觉的表面缺陷检测研究进展OACHSSCDCSTPCD

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.