Comparative Analysis of Deep Learning Based Steel Surface Defect Detection under Different Lighting Conditions

Abstract

The automatic inspection and detection of defects on flat steel surfaces is important for product manufacturers as well as for users of these products. Since industrial products are used in various fields such as transportation, energy production, food production, the inspection of these products is a difficult problem today. Traditional methods such as image processing or machine learning used to provide inspections give successful results in detecting sufficiently illuminated, strong contrast or obvious defects. In this study, flat steel surface defects, which is an industrial product, are discussed. The aim of the study is to test the robustness of deep learning methods under different illumination conditions and to determine their response. For this purpose, four popular YOLO object detection methods are used. Because of the different illuminations applied on the data set, the changes in the defect detection algorithms have been observed and the results are shared. Experimental results clearly demonstrate the effect of lighting on model success. In addition, the proposed approach presents an improvement in terms of both detection rate and frame rate compared to studies in the literature.