Online PCB Defect Detector On A New PCB Defect Dataset

TL;DR

This paper introduces a deep learning model with a novel group pyramid pooling module for accurate PCB defect detection, validated on a new dataset with high performance and real-time speed.

Contribution

The work presents a new deep model with a group pyramid pooling module and a novel dataset for PCB defect detection, improving accuracy and efficiency.

Findings

Achieved 98.6% mAP on DeepPCB dataset

Processed images at 62 FPS in real-time

Validated effectiveness over previous methods

Abstract

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.