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An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet

Author

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  • Yuanyuan Xu
  • Genke Yang
  • Jiliang Luo
  • Jianan He

Abstract

Electronic component recognition plays an important role in industrial production, electronic manufacturing, and testing. In order to address the problem of the low recognition recall and accuracy of traditional image recognition technologies (such as principal component analysis (PCA) and support vector machine (SVM)), this paper selects multiple deep learning networks for testing and optimizes the SqueezeNet network. The paper then presents an electronic component recognition algorithm based on the Faster SqueezeNet network. This structure can reduce the size of network parameters and computational complexity without deteriorating the performance of the network. The results show that the proposed algorithm performs well, where the Receiver Operating Characteristic Curve (ROC) and Area Under the Curve (AUC), capacitor and inductor, reach 1.0. When the FPR is less than or equal level, the TPR is greater than or equal to 0.99; its reasoning time is about 2.67 ms, achieving the industrial application level in terms of time consumption and performance.

Suggested Citation

  • Yuanyuan Xu & Genke Yang & Jiliang Luo & Jianan He, 2020. "An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-11, October.
    • Handle: RePEc:hin:jnlmpe:2940286
    DOI: 10.1155/2020/2940286
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    Cited by:

    1. Xu, Yuanyuan & Yang, Genke & Luo, Jiliang & He, Jianan & Sun, Haixin, 2022. "A multi-location short-term wind speed prediction model based on spatiotemporal joint learning," Renewable Energy, Elsevier, vol. 183(C), pages 148-159.
    2. Praneel Chand & Mansour Assaf, 2024. "An Empirical Study on Lightweight CNN Models for Efficient Classification of Used Electronic Parts," Sustainability, MDPI, vol. 16(17), pages 1-18, September.
    3. Praneel Chand, 2023. "A Low-Resolution Used Electronic Parts Image Dataset for Sorting Application," Data, MDPI, vol. 8(1), pages 1-11, January.

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