Weak Supervised Surface Defect Detection Method Based on Selective Search and CAM

Abstract

Due to the large scale variation of surface defects of different types of strip steel, there are limitations in using threshold segmentation to locate objects, we propose a surface defect detection algorithm combining selective search and class activation mapping (CAM) to improve objects localization. First, we use selective search to generate defect bounding box in the image, and predicts the classification and CAM of the defect in the image through the trained model. Then, in the defect detection, filter the bounding box with the classification information of the defect as priori knowledge. We only retain the bounding box that approximate the shape of the defect and map the filtered defect bounding box to the CAM of the corresponding defect. Finally, select the bounding box with the highest score as a detection result. Experiment results show that the proposed method can achieve a mean average precision of 91.1% on our dataset. And it can more accurately locate defects in the image. Compared with traditional CAM, our method has more excellent detection performance in surface defect detection applications of strip steel.