SD10: a large-scale and multi-scale benchmark dataset for steel surface defect detection

In the development of surface defect detection algorithms for steel, benchmark datasets play a crucial role. However, the widely used benchmark datasets generally suffer from limited scale, insufficient coverage of defect categories, and poor annotation accuracy, making it difficult to meet the growing demand for surface defect detection in steel. To this end, this study constructed and annotated a large-scale, multi-scale benchmark dataset SD10 (Steel Defect Detection) covering 10 typical types of defects: Crazing, Inclusion, Patches, Pitted Surface, Rolled in Scale, Scratches, Blowhole, Break, Fray, and Uneven. The dataset aims to break through the application bottleneck of existing datasets, provide high-quality and reliable data support for in-depth research and optimization iteration of steel surface defect detection algorithms.