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YOLOV4_CSPBi: Enhanced Land Target Detection Model

Author

Listed:
  • 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)

  • Jianqiang Li

    (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)

  • Zhengtong Yin

    (College of Resource and Environment Engineering, Guizhou University, Guiyang 550025, China)

  • Shan Liu

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

  • Wenfeng Zheng

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

Abstract

The identification of small land targets in remote sensing imagery has emerged as a significant research objective. Despite significant advancements in object detection strategies based on deep learning for visible remote sensing images, the performance of detecting a small and densely distributed number of small targets remains suboptimal. To address this issue, this study introduces an improved model named YOLOV4_CPSBi, based on the YOLOV4 architecture, specifically designed to enhance the detection capability of small land targets in remote sensing imagery. The proposed model enhances the traditional CSPNet by redefining its channel partitioning and integrating this enhanced structure into the neck part of the YOLO network model. Additionally, the conventional pyramid fusion structure used in the traditional BiFPN is removed. By integrating a weight-based bidirectional multi-scale mechanism for feature fusion, the model is capable of effectively reasoning about objects of various sizes, with a particular focus on detecting small land targets, without introducing a significant increase in computational costs. Using the DOTA dataset as research data, this study quantifies the object detection performance of the proposed model. Compared with various baseline models, for the detection of small targets, its AP performance has been improved by nearly 8% compared with YOLOV4. By combining these modifications, the proposed model demonstrates promising results in identifying small land targets in visible remote sensing images.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:9:p:1813-:d:1244467
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    References listed on IDEAS

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    1. Sara Mastrorosa & Mattia Crespi & Luca Congedo & Michele Munafò, 2023. "Land Consumption Classification Using Sentinel 1 Data: A Systematic Review," Land, MDPI, vol. 12(4), pages 1-25, April.
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    Cited by:

    1. Samuel Bimenyimana & Chen Wang & Godwin Norense Osarumwense Asemota & Jeanne Paula Ihirwe & Mucyo Ndera Tuyizere & Fidele Mwizerwa & Yiyi Mo & Martine Abiyese, 2024. "Geospatial Analysis of Wind Energy Siting Suitability in the East African Community," Sustainability, MDPI, vol. 16(4), pages 1-32, February.
    2. Prosenjit Barman & Sheikh Mustak & Monika Kuffer & Sudhir Kumar Singh, 2023. "Transfer-Ensemble Learning: A Novel Approach for Mapping Urban Land Use/Cover of the Indian Metropolitans," Sustainability, MDPI, vol. 15(24), pages 1-26, December.
    3. Muhammad Rashid & Saif Haider & Muhammad Umer Masood & Chaitanya B. Pande & Abebe Debele Tolche & Fahad Alshehri & Romulus Costache & Ismail Elkhrachy, 2023. "Sustainable Water Management for Small Farmers with Center-Pivot Irrigation: A Hydraulic and Structural Design Perspective," Sustainability, MDPI, vol. 15(23), pages 1-29, November.

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