Detection of Manufacturing Defects in Steel Using Deep Learning With Explainable Artificial Intelligence

Guaranteeing steel quality is a crucial step in the steel manufacturing process. Many manufacturing industries still resort to manual visual inspection, which is inefficient and time-consuming. Industries have not fully adopted automated visual inspection due to inaccuracies, the variability of real-world manufacturing environments, and a lack of familiarity with the decisions output by the automated technology. Nonetheless, the implementation of automated defect detection systems can substantially enhance the quality of the end product. In particular, Convolutional Neural Networks (CNNs) have demonstrated exceptional abilities in image classification and segmentation tasks. There is still significant room for improvement in terms of the detection and localization accuracy, the robustness of the algorithms, and their practicality of use. This paper employs and evaluates different semantic segmentation approaches with U-Net and Feature Pyramid Network (FPN) architecture utilizing different CNN backbones. Additionally, the study enhances the model's robustness by utilizing various data augmentation techniques. Moreover, the study incorporates Explainable Artificial Intelligence (XAI) to provide insights into the decision-making processes of deep neural networks, bridging the gap in understanding. The contributions of this work are in improving the practicality, interpretability, and robustness of CNN-based algorithms for steel surface defect detection and segmentation.