Deep Learning Based Steel Plate Surface Defect Detection with Precise RoI Pooling

Purpose: The purpose of this study is to attempt to improve the steel industry by suggesting ways to improve the quality of steel products.Methods: An attempt was made to improve the detection of defect data on the surface of steel plates through data augmentation techniques and region-of-interest pooling techniques. The tools used in this study are categorized into three dimensions: object detection model, region of interest pooling, and Mixup data augmentation.Results: The results of this study are as follows We used Mixup and Precise RoI Pooling to improve the detection performance of steel surface. We studied the effect of Mixup on the detection of steel surface defects through experiments, and found that the detection performance of certain classes is degraded when Mixup is applied. To solve this problem, we found that it is effective to apply Precise RoI pooling to improve the detection performance of the model without applying Mixup, and then we integrated Precise RoI pooling and Mixup to improve the detection performance of steel surface defects for all classes. The proposed method was found to take 0.01 seconds per sheet, which is faster than visual inspection, which takes 6 seconds per sheet.Conclusion: Improved detection of steel plate surface defects