2025/04/29 by Irina Ruzavina, L Theis, Ruzavina, Irina +13
Computer Science · Engineering · Environmental Science · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #Erosion and Abrasive Machining #FOS: Computer and information sciences #Industrial Vision Systems and Defect Detection #Neural and Evolutionary Computing (cs.NE)
paper · pdf · doi:10.48550/arxiv.2504.20510
openalex publication_date 2025/04/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.