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Detecting and neutralizing tanks from long distances using Unmanned Aerial Vehicles (UAVs) is an emerging research area with the potential to significantly enhance military capabilities and reduce loss of life. Despite this potential, the domain remains relatively underexplored, especially in specific terrains and operational contexts. This study presents the development of a real-time tank detection and targeting system. The proposed end-to-end framework integrates advanced machine learning algorithms, image processing techniques, and precision targeting mechanisms. The system operates in three phases: Phase 1 detects tanks using object detection algorithms such as YOLOv7; Phase 2 enhances detection accuracy at long distances through the Slicing-Aided Hyper Inference (SAHI) algorithm; and Phase 3 applies the StrongSORT tracking algorithm for continuous target tracking and engagement. Experimental evaluation on the VisDrone dataset demonstrates the system’s effectiveness in reducing operational risks and improving mission success rates, achieving a mean Average Precision (mAP) of 0.88 at IoU 0.5, with performance decreasing to 0.59 for mAP@0.5:0.95.

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Leveraging YOLO and SAHI for Accurate Small Tank Detection in UAV Systems

  • 会议论文

  • 发表日期：

    2026年

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  • Van Hieu Bui,
  • Tuan Anh Luong,
  • Thao Linh Tran,
  • Hoang Nam Nguyen Ba,
  • Phuong Nguyen Nguyen

摘要

Detecting and neutralizing tanks from long distances using Unmanned Aerial Vehicles (UAVs) is an emerging research area with the potential to significantly enhance military capabilities and reduce loss of life. Despite this potential, the domain remains relatively underexplored, especially in specific terrains and operational contexts. This study presents the development of a real-time tank detection and targeting system. The proposed end-to-end framework integrates advanced machine learning algorithms, image processing techniques, and precision targeting mechanisms. The system operates in three phases: Phase 1 detects tanks using object detection algorithms such as YOLOv7; Phase 2 enhances detection accuracy at long distances through the Slicing-Aided Hyper Inference (SAHI) algorithm; and Phase 3 applies the StrongSORT tracking algorithm for continuous target tracking and engagement. Experimental evaluation on the VisDrone dataset demonstrates the system’s effectiveness in reducing operational risks and improving mission success rates, achieving a mean Average Precision (mAP) of 0.88 at IoU 0.5, with performance decreasing to 0.59 for mAP@0.5:0.95.