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CGBi_YOLO: Lightweight Land Target Detection Network

Author

Listed:
  • Ruiyang Wang

    (School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China)

  • Siyu Lu

    (School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China)

  • Jiawei Tian

    (School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China)

  • Lirong Yin

    (Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA)

  • Lei Wang

    (Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA)

  • Xiaobing Chen

    (School of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA)

  • Wenfeng Zheng

    (School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China)

Abstract

Object detection algorithms for optical remote sensing images often face challenges in computational efficiency, particularly when detecting small and densely packed targets. This paper introduces CGBi_YOLO, a novel lightweight land target detection network designed to optimize computational resource utilization while maintaining detection capabilities for small-scale targets. Our approach incorporates an innovative lightweight optimization strategy featuring a new lightweight backbone feature extraction network: CSPGhostNet. This model significantly enhances the detection ability of small objects within optical remote sensing images without increasing computational demands. The efficacy of the proposed model is validated through rigorous experimentation on the DOTA dataset. Compared to the baseline model, CGBi_YOLO achieves a 30% reduction in parameters and a 36% increase in inference speed. The model demonstrates exceptional performance in handling small and densely packed targets within optical remote sensing images, showcasing its potential for real-world applications in fields such as environmental monitoring, urban planning, and disaster management.

Suggested Citation

  • Ruiyang Wang & Siyu Lu & Jiawei Tian & Lirong Yin & Lei Wang & Xiaobing Chen & Wenfeng Zheng, 2024. "CGBi_YOLO: Lightweight Land Target Detection Network," Land, MDPI, vol. 13(12), pages 1-19, November.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:12:p:2060-:d:1533984
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    References listed on IDEAS

    as
    1. Lirong Yin & Lei Wang & Jianqiang Li & Siyu Lu & Jiawei Tian & Zhengtong Yin & Shan Liu & Wenfeng Zheng, 2023. "YOLOV4_CSPBi: Enhanced Land Target Detection Model," Land, MDPI, vol. 12(9), pages 1-17, September.
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