深度学习
计算机科学
人工智能
曲面(拓扑)
无监督学习
钥匙(锁)
机器学习
数学
几何学
计算机安全
标识
DOI:10.1109/itca52113.2020.00049
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI