IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v15y2023i3p114-d1101445.html
   My bibliography  Save this article

Research on Spaceborne Target Detection Based on Yolov5 and Image Compression

Author

Listed:
  • Qi Shi

    (Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201304, China
    University of Chinese Academy of Sciences, Beijing 100039, China)

  • Daheng Wang

    (China Satellite Network Group Co., Ltd., Beijing 100000, China)

  • Wen Chen

    (Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201304, China
    University of Chinese Academy of Sciences, Beijing 100039, China)

  • Jinpei Yu

    (Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201304, China
    University of Chinese Academy of Sciences, Beijing 100039, China)

  • Weiting Zhou

    (School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China)

  • Jun Zou

    (School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China)

  • Guangzu Liu

    (School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China)

Abstract

Satellite image compression technology plays an important role in the development of space science. As optical sensors on satellites become more sophisticated, high-resolution and high-fidelity satellite images will occupy more storage. This raises the required transmission bandwidth and transmission rate in the satellite–ground data transmission system. In order to reduce the pressure from image transmission on the data transmission system, a spaceborne target detection system based on Yolov5 and a satellite image compression transmission system is proposed in this paper. It can reduce the pressure on the data transmission system by detecting the object of interest and deciding whether to transmit. An improved Yolov5 network is proposed to detect the small target on the high-resolution satellite image. Simulation results show that the improved Yolov5 network proposed in this paper can detect specific targets in real satellite images, including aircraft, ships, etc. At the same time, image compression has little effect on target detection, so detection complexity can be effectively reduced and detection speed can be improved by detecting the compressed images.

Suggested Citation

  • Qi Shi & Daheng Wang & Wen Chen & Jinpei Yu & Weiting Zhou & Jun Zou & Guangzu Liu, 2023. "Research on Spaceborne Target Detection Based on Yolov5 and Image Compression," Future Internet, MDPI, vol. 15(3), pages 1-17, March.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:3:p:114-:d:1101445
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/15/3/114/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/15/3/114/
    Download Restriction: no
    ---><---

    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:jftint:v:15:y:2023:i:3:p:114-:d:1101445. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.