IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i15p1766-d601952.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/15/1766/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/15/1766/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohammad Motasem ALrfai & Danilah Binti Salleh & Waeibrorheem Waemustafa, 2022. "Empirical Examination of Credit Risk Determinant of Commercial Banks in Jordan," Risks, MDPI, vol. 10(4), pages 1-11, April.
    2. Lesly Lisset Ortiz-Cerezo & Alin Andrei Carsteanu & Julio Bernardo Clempner, 2022. "Sharpe-Ratio Portfolio in Controllable Markov Chains: Analytic and Algorithmic Approach for Second Order Cone Programming," Mathematics, MDPI, vol. 10(18), pages 1-13, September.
    3. Michał Bernardelli & Zbigniew Korzeb & Paweł Niedziółka, 2022. "Does Fossil Fuel Financing Affect Banks’ ESG Ratings?," Energies, MDPI, vol. 15(4), pages 1-19, February.
    4. Zhiwu Zhou & Julián Alcalá & Víctor Yepes, 2021. "Optimized Application of Sustainable Development Strategy in International Engineering Project Management," Mathematics, MDPI, vol. 9(14), pages 1-29, July.
    5. Ángel Acevedo-Duque & Romel Gonzalez-Diaz & Elena Cachicatari Vargas & Anherys Paz-Marcano & Sheyla Muller-Pérez & Guido Salazar-Sepúlveda & Giulia Caruso & Idiano D’Adamo, 2021. "Resilience, Leadership and Female Entrepreneurship within the Context of SMEs: Evidence from Latin America," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
    6. Nicolás Caselli & Ricardo Soto & Broderick Crawford & Sergio Valdivia & Rodrigo Olivares, 2021. "A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique," Mathematics, MDPI, vol. 9(16), pages 1-28, August.
    7. Veronica Piccialli & Antonio M. Sudoso & Angelika Wiegele, 2022. "SOS-SDP: An Exact Solver for Minimum Sum-of-Squares Clustering," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 2144-2162, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:15:p:1766-:d:601952. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.