Character Identification for Integrated Circuit Components on Printed Circuit Boards Using Deep Learning

Jia, Xibin; Liu, Zihao

doi:10.1007/s42835-021-00885-4

Cited by 3 publications

(1 citation statement)

References 23 publications

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“… Lu et al (2018) proved the effectiveness of fused features in defect classification problems, but their proposed algorithm cannot detect specific defect positions on the PCB surface. Jia & Liu (2022) improved the LeNet-5 network model for PCB character defect detection by changing the layer depth to examine the impact of different network architectures on model efficiency and adding a combination classifier in the fully connected layer to enhance the feature expression performance. However, this proposed method does not reduce the need for human intervention during the training process, and it is still a significant challenge to study the segmentation of overlapping character clusters.…”

Section: Introductionmentioning

confidence: 99%

Defect identification of bare printed circuit boards based on Bayesian fusion of multi-scale features

Han,

Li,

Wang

et al. 2024

PeerJ Computer Science

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The aim of this article is to propose a defect identification method for bare printed circuit boards (PCB) based on multi-feature fusion. This article establishes a description method for various features of grayscale, texture, and deep semantics of bare PCB images. First, the multi-scale directional projection feature, the multi-scale grey scale co-occurrence matrix feature, and the multi-scale gradient directional information entropy feature of PCB were extracted to build the shallow features of defect images. Then, based on migration learning, the feature extraction network of the pre-trained Visual Geometry Group16 (VGG-16) convolutional neural network model was used to extract the deep semantic feature of the bare PCB images. A multi-feature fusion method based on principal component analysis and Bayesian theory was established. The shallow image feature was then fused with the deep semantic feature, which improved the ability of feature vectors to characterize defects. Finally, the feature vectors were input as feature sequences to support vector machines for training, which completed the classification and recognition of bare PCB defects. Experimental results show that the algorithm integrating deep features and multi-scale shallow features had a high recognition rate for bare PCB defects, with an accuracy rate of over 99%.

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Section: Introductionmentioning

confidence: 99%