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Improving the Deeplabv3+ Model with Attention Mechanisms Applied to Eye Detection and Segmentation

Author

Listed:
  • Chih-Yu Hsu

    (School of Transportation, Fujian University of Technology, Fuzhou 350118, China
    Intelligent Transportation System Research Center, Fujian University of Technology, Fuzhou 350118, China)

  • Rong Hu

    (Fujian Provincial Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, China
    Fujian Provincial Key Laboratory of Big Data Mining and Applications, School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China)

  • Yunjie Xiang

    (Fujian Provincial Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, China)

  • Xionghui Long

    (School of Mechatronic Engineering, Guangzhou Polytechnic College, Guangzhou 510091, China)

  • Zuoyong Li

    (Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou 350108, China)

Abstract

Research on eye detection and segmentation is even more important with mask-wearing measures implemented during the COVID-19 pandemic. Thus, it is necessary to build an eye image detection and segmentation dataset (EIMDSD), including labels for detecting and segmenting. In this study, we established a dataset to reduce elaboration for chipping eye images and denoting labels. An improved DeepLabv3+ network architecture (IDLN) was also proposed for applying it to the benchmark segmentation datasets. The IDLN was modified by cascading convolutional block attention modules (CBAM) with MobileNetV2. Experiments were carried out to verify the effectiveness of the EIMDSD dataset in human eye image detection and segmentation with different deep learning models. The result shows that the IDLN model achieves the appropriate segmentation accuracy for both eye images, while the UNet and ISANet models show the best results for the left eye data and the right eye data among the tested models.

Suggested Citation

  • Chih-Yu Hsu & Rong Hu & Yunjie Xiang & Xionghui Long & Zuoyong Li, 2022. "Improving the Deeplabv3+ Model with Attention Mechanisms Applied to Eye Detection and Segmentation," Mathematics, MDPI, vol. 10(15), pages 1-20, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2597-:d:871755
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    Cited by:

    1. Yunjie Xiang & Rong Hu & Yong Xu & Chih-Yu Hsu & Congliu Du, 2023. "Gaussian Weighted Eye State Determination for Driving Fatigue Detection," Mathematics, MDPI, vol. 11(9), pages 1-24, April.

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