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Research on salient object detection algorithm for complex electrical components

Author

Listed:
  • Jinyu Tian

    (Wuyi University)

  • Zhiqiang Zeng

    (Wuyi University)

  • Zhiyong Hong

    (Wuyi University)

  • Dexin Zhen

    (Wuyi University)

Abstract

Due to the complexity of electrical components, traditional edge detection methods cannot always accurately extract key edge features of them. Therefore, this study constructs a dataset of complex electrical components and proposes a Step-by-Level Multi-Scale Extraction, Fusion, and Refinement Network (SMFRNet) that is based on the salient object detection algorithm. As detailed features includes a wealth of texture and shape characteristics that are related to edges, so the Hierarchical Deep Aggregation U-block (HDAU) is incorporated in the encoder as a means of capturing more details through hierarchical aggregation. Meanwhile, the proposed Multi-Scale Pyramid Convolutional Fusion (MPCF) and Fusion Attention Structure (FAS) achieve step-by-level feature refinement to obtain finer edges. In order to address the issues of imbalanced pixel categories and the difficulty in separating edge pixels, a hybrid loss function is also constructed. The experimental results indicate that this method outperforms nine state-of-the-art algorithms, enabling the extraction of high-precision key edge features. It provides a reliable method for key edge extraction in complex electrical components and provides important technical support for automated components measurement.

Suggested Citation

  • Jinyu Tian & Zhiqiang Zeng & Zhiyong Hong & Dexin Zhen, 2025. "Research on salient object detection algorithm for complex electrical components," Journal of Intelligent Manufacturing, Springer, vol. 36(6), pages 4005-4023, August.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:6:d:10.1007_s10845-024-02434-y
    DOI: 10.1007/s10845-024-02434-y
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    References listed on IDEAS

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    1. Durga Prasad Penumuru & Sreekumar Muthuswamy & Premkumar Karumbu, 2020. "Identification and classification of materials using machine vision and machine learning in the context of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1229-1241, June.
    2. Olatomiwa Badmos & Andreas Kopp & Timo Bernthaler & Gerhard Schneider, 2020. "Image-based defect detection in lithium-ion battery electrode using convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 885-897, April.
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