High Efficient Single-stage Steel Surface Defect Detection

Abstract

To date, deep learning has been widely introduced in many fields, including object detection, medical imaging, and automation. One important application that uses deep learning based object detection is detecting defects by simply evaluating the image of an object. Such systems must be accurate, robust and efficient. Single-stage and two-stage object detection are two main approaches used in defect detection systems. A revised version of the popular object detection method called single shot multi-box detector (SSD) and the residual network (ResNet) offer a two-stage method to automatically detect defects with higher precision but has shown room for improvement with regard to speed performance. Therefore, in this paper, we propose a fully automatic pipeline for detecting defects, especially on steel surfaces. A novel transformation of the two-stage defect detection process into a more efficient single-stage detection process was introduced by utilizing a state-of-the-art method called RetinaNet. In addition, we leverage a feature pyramid network (FPN) and focal loss optimization to solve the small object detection problem and to deal with imbalanced background-foreground samples issue, respectively. Experimental results show that the proposed single-stage pipeline can achieve high accuracy and faster speed in steel surface defect detection.