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WPC-SS: multi-label wear particle classification based on semantic segmentation

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Abstract

In this paper, a novel wear particle online images multi-label classification based on semantic segmentation (WPC-SS) is proposed. In this model, both semantic labels and class labels are applied to guide network training, which make the regions with wear particles attain more attention during the process of network training. It solves the problem that it is difficult to classify the small wear particles when they are compared with the background in the online image. In addition, chain channel attention and class attention unit are added to optimize the network to improve the recognition accuracy. The important channels of the network are monitored by chain channel attention, so that the extracted features can be better prepared for the subsequent classification work. Class attention unit can refine the segmentation results and further optimize the classification results. Comparison experiments are executed with the standard image classification method (multi-CNN). The results of experiments show that WPC-SS model surpasses the standard image classification methods in solving the problem of multi-label classification of online wear particle images.

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Funding

This paper is sponsored by the National Study Abroad Fund of China, Fundamental Research Funds for the Central Universities (FRF-GF-20-16B) and supported by The National Key Research and Development Program of China (2017YFB1002304).

Author information

Authors and Affiliations

  1. Department of Computer, School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing, 100083, China

    Suli Fan, Taohong Zhang, Xuxu Guo & Aziguli Wulamu

  2. Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, 100083, China

    Suli Fan, Taohong Zhang & Aziguli Wulamu

  3. QingGong College, North China University of Science and Technology, Tangshan, 064000, Hebei, China

    Ying Zhang

Authors
  1. Suli Fan
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  2. Taohong Zhang
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  3. Xuxu Guo
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  4. Ying Zhang
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  5. Aziguli Wulamu
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Contributions

SF and TZ performed the data analyses, the experiment and wrote the manuscript. AW contributed significantly to analysis and manuscript preparation. XG and YZ helped perform the analysis with constructive discussion.

Corresponding authors

Correspondence to Taohong Zhang or Aziguli Wulamu.

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Fan, S., Zhang, T., Guo, X. et al. WPC-SS: multi-label wear particle classification based on semantic segmentation. Machine Vision and Applications 33, 43 (2022). https://doi.org/10.1007/s00138-022-01287-w

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  • DOI: https://doi.org/10.1007/s00138-022-01287-w

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