Due to the rapid development of printed circuit board (PCB) design technology, inspection of PCB surface defects has become an increasingly critical issue. The classification of PCB defects facilitates the root causes of detectsa?? identification. As PCB defects may be intensive, the actual PCB classification should not be considered as a binary or multi-category problem. This type of problem is called multi-label classification problem. Recently, as one of the deep learning frameworks, a convolutional neural network (CNN) has a major breakthrough in many areas of image processing, especially in the image classification. This study proposes a multi-task CNN model to handle the multi-label learning problem by defining each label learning as a binary classification task. In this study, the multi-label learning is transformed into multiple binary classification tasks by customising the loss function. Extensive experiments demonstrate that the proposed method achieves great performance on the dataset of defects.
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