An Improved YOLOv3 Network for PCB Defect Detection

Abstract

Different defects with various features and shapes may appeared in one printed circuit board (PCB), making a great challenge for defect detection. To deal with the contradiction between the speed and accuracy of current deep learning networks, we put forward a MobileNet-YOLOv3 network for PCB defect detection. The MobileNet is introduced to replace the backbone network Darknet-53 of YOLOv3, so as to obtain a lightweight network. Meanwhile, the feature pyramid network (FPN) of YOLOv3 is improved by channel reorganization and downsampling to obtain higher accuracy. Experiments on public data set images with 6 types of defects are conducted, comparative results reveal that the proposed MobileNet-YOLOv3 can achieve higher accuracy than YOLOv3 and YOLOv3-Tiny. According to the frames per second factor compared with YOLOv3-Tiny and YOLOv3, the proposed network is faster than YOLOv3.