Enhanced Defect Classification With CNN’s And path Aggregation Network

Steel is one of the metals used in most industrial sectors. Any damage to steel will have serious consequences on the industry's economy. Therefore, defect detection has become one of the most important and competitive tasks in steel quality control, either through classification or defect detection, which combines classification and localization. This paper proposes a new architecture for steel surface defect classification based on the modified MobileNetv3 as a backbone to extract the semantic information along the horizontal and vertical spatial direction that is missing extracted with the local receptive field or the global receptive field, moreover, the latter is integrated into the path aggregation network which enhanced by an improved double bottom-up pathway to aggregate features with multiple resolutions to achieve strong classification capability, our experiments in Neu-Det show the classification improvement of the proposed architecture with other CNN's architectures.