  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.
