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- W4313201975 abstract "In the SMT process, after component placement, checking the quality of component placement on the PCB board is a basic requirement for quality control of the motherboard. In this paper, we propose a deep learning-based classification method to identify the quality of component placement. This is a comparison method and the novelty is that the siamese network is trained to extract the features of the standard placement component map and the placement component map to be inspected and output the probability of similarity between the two to determine the goodness of the image to be inspected. Compared to traditional hand-crafted features, features extracted using convolutional neural networks are more abstract and robust. In addition, during training, the concatenated network pairs the sample images to expand the amount of training data, increasing the robustness of the network and reducing the risk of overfitting. The experimental results show that this method has better results than the general model for the classification of placement component images." @default.
- W4313201975 created "2023-01-06" @default.
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- W4313201975 date "2022-01-01" @default.
- W4313201975 modified "2023-09-26" @default.
- W4313201975 title "SMT Component Defection Reassessment Based on Siamese Network" @default.
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- W4313201975 doi "https://doi.org/10.1007/978-981-19-9195-0_7" @default.
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