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.在钢铁表面缺陷检测算法的研发进程中，基准数据集发挥着至关重要的作用。然而当前广泛使用的基准数据集普遍存在样本规模有限、缺陷类别覆盖不足、标注精度欠佳等问题，难以满足钢铁表面缺陷检测领域日益增长的应用需求。为此，本研究构建并标注了一款大规模、多尺度的基准数据集SD10（钢铁缺陷检测数据集，Steel Defect Detection），涵盖10类典型缺陷：裂纹（Crazing）、夹杂物（Inclusion）、斑块（Patches）、麻点表面（Pitted Surface）、氧化铁皮压入（Rolled in Scale）、划痕（Scratches）、气孔（Blowhole）、断裂（Break）、起毛（Fray）以及不均匀缺陷（Uneven）。本数据集旨在突破现有基准数据集的应用瓶颈，为钢铁表面缺陷检测算法的深入研究与优化迭代提供高质量、可靠的数据支撑。

搜集汇总

背景与挑战

背景概述

SD10是一个大规模、多尺度的钢铁表面缺陷检测基准数据集，覆盖了10种典型缺陷类型，如Crazing和Inclusion。该数据集旨在解决现有数据集规模有限、缺陷类别覆盖不足和标注准确性差的问题，为算法研究和优化提供高质量数据支持。