Classification of Surface Defects on Galvanized Cold-Rolled Steel Sheets Using Data-Driven Fault Model With Attention Mechanism

Abstract

In the production of the galvanized cold-rolled steel sheets used for stamping car body parts, in-situ and real-time defective detecting is crucial for quality control, in which various types of defects will inevitably occur. It is challenging to improve the accuracy of defect image classification by appropriate means to assist the manual screening process better. Defects under actual production conditions are often not prominent enough in defect characteristics, and there may be a significant similarity between different defect categories. To eliminate this weakness, we propose a data-driven faulty detection model named Steel Faulty Detection Attention Net (SFDANet) that uses images of the galvanized steel surface as input to identify whether the product is qualified and automatic classification of defect types instantaneously. This method can shorten product inspection time and improve production line efficiency automatically. In addition, the attention mechanism is utilized, enhancing the performance of SFDANet. Compared with the baseline that applied the ResNet method, SFDANet achieves a noticeable improvement in the classification accuracy of the test data. The well-trained model can successfully show an improved performance than the baseline models on the multiple types of faulty. Enhanced by SFDANet with high classification accuracy, the defect rate of products is significantly reduced, and the production speed of the production line is significantly improved.