Automating the quality control of shot-blasted steel surfaces is crucial for improving manufacturing efficiency and consistency. This study presents a dataset of 1654 labeled RGB images (512x512) of steel surfaces, classified as either "ready for paint" or "needs shot-blasting." The dataset captures real-world surface defects, including discoloration, welding lines, scratches and corrosion, making it well-suited for training computer vision models. Additionally, three classification approaches were evaluated: Compact Convolutional Transformers (CCT), Support Vector Machines (SVM) with ResNet-50 feature extraction, and a Convolutional Autoencoder (CAE). The supervised methods (CCT and SVM) achieve 95% classification accuracy on the test set, with CCT leveraging transformer-based attention mechanisms and SVM offering a computationally efficient alternative. The CAE approach, while less effective, establishes a baseline for unsupervised quality control. We present interpretable decision-making by all three neural networks, allowing industry users to visually pinpoint problematic regions and understand the model's rationale. By releasing the dataset and baseline codes, this work aims to support further research in defect detection, advance the development of interpretable computer vision models for quality control, and encourage the adoption of automated inspection systems in industrial applications.
View on arXiv@article{ruzavina2025_2504.20510,
title={ SteelBlastQC: Shot-blasted Steel Surface Dataset with Interpretable Detection of Surface Defects },
author={ Irina Ruzavina and Lisa Sophie Theis and Jesse Lemeer and Rutger de Groen and Leo Ebeling and Andrej Hulak and Jouaria Ali and Guangzhi Tang and Rico Mockel },
journal={arXiv preprint arXiv:2504.20510},
year={ 2025 }
}