Abstract
Steel-manufacturing sites are extremely harsh and dangerous environments. Visibility is reduced by dust, water vapor, oil, and low light conditions. Additionally, the steel products from hot furnaces have surface temperatures ranging from 1200 to 800 degrees celsius and weigh several to tens of tons. It is extremely dangerous for humans to visually inspect these steel products in such environments. Therefore, the use of automatic inspection equipment for steel surfaces is essential. Initially, image-processing methods were used, but with recent advances in deep learning, deep-learning-based methods are also being applied in this field. However, the method currently widely used involves utilizing existing models through transfer learning, which inevitably causes curvature of the input image data and limits performance. Furthermore, previous studies have focused on 2D sheet metal products, and there has been no research on three-dimensional geometric products. In this study, we propose dataset generation through geometric transformations that parameterize the structure of the steel surface defect detector hardware, along with a performance-based model optimization algorithm. In validation experiments, an average F1 score of 0.932 and an average area under curve of 0.99 were obtained, implying that the proposed algorithm has near-ideal performance.