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YOLOMask, an Instance Segmentation Algorithm Based on Complementary Fusion Network

Author

Listed:
  • Jiang Hua

    (Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Tonglin Hao

    (School of Automation, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Liangcai Zeng

    (Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Gui Yu

    (Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
    School of Mechanical and Electrical Engineering, Huanggang Normal University, Huanggang 438000, China)

Abstract

Object detection and segmentation can improve the accuracy of image recognition, but traditional methods can only extract the shallow information of the target, so the performance of algorithms is subject to many limitations. With the development of neural network technology, semantic segmentation algorithms based on deep learning can obtain the category information of each pixel. However, the algorithm cannot effectively distinguish each object of the same category, so YOLOMask, an instance segmentation algorithm based on complementary fusion network, is proposed in this paper. Experimental results on public data sets COCO2017 show that the proposed fusion network can accurately obtain the category and location information of each instance and has good real-time performance.

Suggested Citation

  • Jiang Hua & Tonglin Hao & Liangcai Zeng & Gui Yu, 2021. "YOLOMask, an Instance Segmentation Algorithm Based on Complementary Fusion Network," Mathematics, MDPI, vol. 9(15), pages 1-12, July.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:15:p:1766-:d:601952
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    References listed on IDEAS

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    1. Caruso, G. & Gattone, S.A. & Fortuna, F. & Di Battista, T., 2021. "Cluster Analysis for mixed data: An application to credit risk evaluation," Socio-Economic Planning Sciences, Elsevier, vol. 73(C).
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