Deep Learning-Based Steel Surface Defect Detection: A Survey

Abstract

With the rapid development of manufacturing, increasingly stringent requirements for material quality and inspection efficiency have promoted the widespread application of deep learning-based nondestructive testing technologies in industrial quality control. In recent years, steel surface defect detection has expanded from conventional inspection scenarios with controlled imaging conditions, such as steel strips, plates, and welds, to more challenging applications involving the inner surfaces of steel pipes, castings, and complex industrial components. This paper reviews recent advances in deep learning-based steel surface defect detection from an industrial application-oriented perspective, with particular emphasis on developments in application scenarios, datasets, methodological frameworks, and research trends. It analyzes representative reviews and their limitations, summarizes publicly available datasets covering conventional flat steel products, pipes, and castings, examines the defect characteristics of different inspection objects, and reviews the development of deep learning and its industrial applications. Furthermore, defect detection and segmentation methods are systematically organized according to practical requirements, including challenging imaging conditions, limited annotated data, small and low-contrast defects, real-time deployment, and cross-domain generalization. Finally, the key challenges, emerging trends, future research directions, and priorities for the next stage of development are discussed.