Journal
IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY
Volume 8, Issue 4, Pages 670-677Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCPMT.2018.2789453
Keywords
Cascaded network; convolutional neural networks (CNNs); regions of interest (ROIs); surface-mount technology (SMT) solder joint inspection; weighted-sum scheme
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Funding
- National Natural Science Foundation of China [61001179, 51305084]
- Guangdong Natural Science Foundation [2015A030312008]
- Guangdong Science and Technology Plan [2015B010104006, 2015B010102014, 2015B090903017, 2015B010124001]
- Guangzhou Science and Technology Plan [201508010001, 201604016022, 201604016064]
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Due to the excellent self-learning ability of deep learning, we propose a novel deep-learning-based method to inspect surface-mount technology (SMT) solder joints in this paper. In contrast to the state-of-the-art learning-based methods in which low-level features are extracted before learning, our method directly implements the inspection task without low-level feature extraction, which is based on a novel cascaded convolutional neural network (CNN). Three kinds of CNNs with different network parameters compose the proposed cascaded CNN. First, one kind of CNN is employed to adaptively learn the regions of interest (ROIs) of SMT solder joint images. Then, both the learned ROIs and the entire solder joint images are fed into the other two kinds of CNNs, respectively. Finally, inspection results are achieved by the learned cascaded CNN. Comparison experiments indicate that our proposed method can achieve more excellent inspection performance for SMT solder joints than that of the state-of-the-art methods.
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