Online PCB Defect Detector On A New PCB Defect Dataset
This work addresses PCB defect detection for manufacturing quality control, presenting an incremental improvement with a new dataset and model.
The paper tackles PCB defect detection by designing a deep model with a novel group pyramid pooling module to handle various defect scales, achieving 98.6% mAP at 62 FPS on a new dataset called DeepPCB.
Previous works for PCB defect detection based on image difference and image processing techniques have already achieved promising performance. However, they sometimes fall short because of the unaccounted defect patterns or over-sensitivity about some hyper-parameters. In this work, we design a deep model that accurately detects PCB defects from an input pair of a detect-free template and a defective tested image. A novel group pyramid pooling module is proposed to efficiently extract features of a large range of resolutions, which are merged by group to predict PCB defect of corresponding scales. To train the deep model, a dataset is established, namely DeepPCB, which contains 1,500 image pairs with annotations including positions of 6 common types of PCB defects. Experiment results validate the effectiveness and efficiency of the proposed model by achieving $98.6\%$ mAP @ 62 FPS on DeepPCB dataset. This dataset is now available at: https://github.com/tangsanli5201/DeepPCB.