Surface Defect Detection with Modified Real-Time Detector YOLOv3

Abstract

In this paper, a modified YOLOv3 net has been proposed for surface defect detection. Different from other pixel-level segmenting methods, YOLOv3 locates the regions of surface defects with bounding rectangles. Compared with conventional detectors, the operating efficiency of YOLOv3 is rather high without generating region proposals by sliding boxes. Although pixel-level details of defects are omitted in the process, the primary information of the location of detects and class labels are extracted by YOLOv3 with high accuracy. This information is sufficient for surface defect inspection, and computational efficiency has been improved, simultaneously. To further light the structure of YOLOv3, loss function optimization and pruning strategy have been adopted in the original YOLOv3. The pruning ratio is determined by the tradeoff between detecting accuracy and computational efficiency. In our experiments, we compared the performance of modified YOLOv3 with several state-of-the-art methods, and modified YOLOv3 achieves the best performance on six types of surface defects in DAGM 2007 dataset.