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Unmanned Aerial Vehicle Target Detection Integrating Computer Deep SORT Algorithm and Wireless Signal

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  • Ao Li

    (Yangzhou Marine Electronic Instrument Institute, China)

Abstract

With the advancement of unmanned aerial vehicle (UAV) technology, accurately detecting UAV targets has become increasingly challenging. This study addresses this issue by proposing a novel UAV target detection method that integrates real-time target tracking algorithms with wireless signal detection technology. Experimental results demonstrate that each improved module positively contributes to the overall detection method. Compared to traditional object detection approaches, the proposed method achieves superior performance on both the VisDrone and COCO datasets, with precision, recall, F1 score, and mean squared error values of 96.07%, 95.84%, 96.33%, and 0.023%, respectively. This integrated approach effectively enhances the accuracy of UAV target detection, offering a robust solution for positioning and tracking in UAV applications.

Suggested Citation

  • Ao Li, 2025. "Unmanned Aerial Vehicle Target Detection Integrating Computer Deep SORT Algorithm and Wireless Signal," International Journal of Interdisciplinary Telecommunications and Networking (IJITN), IGI Global Scientific Publishing, vol. 17(1), pages 1-15, January.
    • Handle: RePEc:igg:jitn00:v:17:y:2025:i:1:p:1-15
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