  To coupe with the difficulties in the process of inspection and
classification of defects in Printed Circuit Board (PCB), other researchers
have proposed many methods. However, few of them published their dataset
before, which hindered the introduction and comparison of new methods. In this
paper, we published a synthesized PCB dataset containing 1386 images with 6
kinds of defects for the use of detection, classification and registration
tasks. Besides, we proposed a reference based method to inspect and trained an
end-to-end convolutional neural network to classify the defects. Unlike
conventional approaches that require pixel-by-pixel processing, our method
firstly locate the defects and then classify them by neural networks, which
shows superior performance on our dataset.

Computer Vision and Pattern Recognition