A Review of Deep Learning Methods for Metal Surface Defect Detection

The surface quality of steel products is paramount to the reliability and safety of modern industries, and deep learning has emerged as the core technology for overcoming the bottlenecks of traditional manual and conventional machine vision inspection methods. To comprehensively review the latest advancements in deep learning‐based applications for metal surface defect detection and to promote further development, this paper analyzes the three core challenges faced in this field and surveys commonly used public datasets. This review first recounts the evolution of mainstream supervised learning algorithms. Subsequently, it elaborates on key strategies for addressing data scarcity, exploring the application of deep learning‐based defect data generation, semi‐supervised learning, and unsupervised learning in anomaly detection, which significantly ameliorates the practical challenge of scarce labeled data in industrial settings. Building upon this, the review focuses on the cutting‐edge directions in the field, deeply analyzing the disruptive opportunities brought by Foundation Models, and highlighting the paradigm shift they introduce. Furthermore, this review explores the integration of artificial intelligence with industrial scenarios, addressing practical implementation challenges and the prospects of autonomous closed‐loop quality control. Finally, a comprehensive conclusion and future outlook are presented.