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).

The source code, including the data loader and example data usage experiments, is at https://github.com/ERNIS-LAB/SteelBlastQC.