SteelBlastQC Dataset

The SteelBlastQC dataset consists of 1654 RGB images (512×512 pixels) of steel surfaces that are either shot-blasted or still need shot-blasting to achieve the required texture, forming a binary classification task. The dataset includes 888 “good” (ready for paint) images and 766 “not-good” (needs shot-blasting) images. As declared by the collaborating manufacturer, the ideally treated surface is clean and uniformly coarse with an average roughness level of SA 2.5. The “not-good” class presents several types of defects to the surface, located by industrial shot-blasting experts. These include: fresh welding lines and cuts, abrasion, corrosion, and discoloration. The presented dataset can be used for training computer vision models for automated metal surface quality control, addressing the lack of publicly available datasets containing images of shot-blasted steel. For convenience and reproducibility, the data were split into train and test (80/20 ratio).SteelBlastQC数据集（SteelBlastQC）包含1654张分辨率为512×512像素的RGB图像，图像内容为钢表面，这些表面要么已完成喷丸处理，要么仍需进行喷丸处理以达到规定的表面质感，该数据集构成一项二分类任务。该数据集包含888张"合格"（可直接进行涂装）图像与766张"不合格"（需进行喷丸处理）图像。据合作制造商声明，理想处理后的钢表面应洁净且粗糙度均匀一致，平均粗糙度等级为SA 2.5。"不合格"类别涵盖多种钢表面缺陷，所有缺陷均由工业喷丸领域专家标注，具体包括：新生成的焊缝与割痕、磨损、腐蚀及变色。本数据集可用于训练用于金属表面自动化质量管控的计算机视觉模型，填补了当前公开可获取的喷丸处理钢表面图像数据集的缺口。为便于使用与结果可复现，数据集已按80:20的比例划分为训练集与测试集。