A PCB Dataset for Defects Detection and Classification
This work addresses the problem of limited data availability for researchers in PCB inspection, though it is incremental as it builds on existing methods.
The authors tackled the lack of publicly available datasets for PCB defect detection by publishing a synthesized dataset of 1386 images with 6 defect types, and they proposed a method that locates and classifies defects using neural networks, showing superior performance on this dataset.
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.