Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network
Bing Hu, Jianhui Wang
Abstract
Defect detection is an essential requirement for quality control in the production of printed circuit boards (PCBs) manufacturing. The traditional defect detection methods have various drawbacks, such as strongly depending on a carefully designed template, highly computational cost, and noise-susceptibility, which pose a significant challenge in a production environment. In this paper, a deep learning-based image detection method for PCB defect detection is proposed. This method builds a new network based on Faster RCNN. We use a ResNet50 with Feature Pyramid Networks as the backbone for feature extraction, to better detect small defects on the PCB. Secondly, we use GARPN to predict more accurate anchors and merge the residual units of ShuffleNetV2. The experimental results show that this method is more suitable for use in production than other PCB defect detection methods. We have also tested in other PCB defects dataset, and experiments have shown that this method is equally valid.