Surface Defect Detection Methods Based on Deep Learning: a Brief Review

Abstract

Surface defect detection techniques based on deep learning have been widely used in various industrial scenarios. This paper reviews the latest works on deep learning-based surface defect detection methods. They are classified into three categories: full-supervised learning model method, unsupervised learning model method, and other methods. The typical methods are further subdivided and compared. The advantages and disadvantages of these methods and their application scenarios are summarized. This paper analyzes three key issues in surface defect detection and introduces common data sets for industrial surface defects. Finally, the future development trend of surface defect detection is predicted.