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An improved YOLO11 UAV toy models target detection model.

Yanting Hu1,2, Xingchen Pu1,2, Sheng Feng1,2

  • 1School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, 611731, China.

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|November 27, 2025
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Summary

Researchers developed a new dataset and an enhanced YOLOv11 model for Unmanned Aerial Vehicle (UAV) military target detection. This approach improves detection accuracy and real-time performance, addressing data scarcity challenges.

Keywords:
Element-wise multiplicationImage enhancementMulti-scenario modelingToy modelsUAV target detectionVanillaNet

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Defense Technology

Background:

  • Military target detection using Unmanned Aerial Vehicles (UAVs) faces significant challenges due to limited training datasets and the need for high real-time precision.
  • Scarcity of publicly available military target datasets stems from data particularity and confidentiality concerns.

Purpose of the Study:

  • To address the limitations in UAV military target detection by creating a novel dataset and an improved detection model.
  • To enhance the robustness and applicability of detection models by simulating real-world image degradation factors.

Main Methods:

  • Construction of the Toy-3 dataset using toy models (ArmoredCar, FixedWing, Tank) photographed in diverse environments.
  • Development of a multi-scene image enhancement method to generate an extended dataset (Toy-3-Enhanced) simulating UAV image acquisition degradations.
  • Proposal of an improved network, Toy-Efficient-YOLOv11, based on the YOLOv11 benchmark model.

Main Results:

  • The Toy-Efficient-YOLOv11 model achieved a mAP50 of 0.982 and mAP50:95 of 0.805 on the Toy-3-Enhanced dataset.
  • The model demonstrated a high processing speed of 333.33 Frames Per Second (FPS).
  • Performance metrics significantly surpassed the YOLOv11n baseline model in both accuracy and real-time detection.

Conclusions:

  • The developed Toy-Efficient-YOLOv11 model offers a superior technical solution for UAV military target detection tasks.
  • The combination of an enhanced dataset and an improved model effectively overcomes challenges related to data scarcity and detection performance.