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IFD-YOLO: A Lightweight Infrared Sensor-Based Detector for Small UAV Targets.

Fu Li1, Xuehan Lv2, Ming Zhao3

  • 1School of Internet of Things Engineering, Wuxi University, Wuxi 214105, China.

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|December 31, 2025
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Summary
This summary is machine-generated.

This study introduces IFD-YOLO, a lightweight infrared detector for unmanned aerial vehicles (UAVs). It significantly improves small target detection accuracy and efficiency for real-time surveillance.

Keywords:
DyGhostUAVYOLOv11infraredsmall target

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Aerospace Engineering

Background:

  • Small target detection in infrared imagery from UAVs is crucial for surveillance.
  • Challenges include small target size, low signal-to-noise ratio, and limited UAV computational resources.

Purpose of the Study:

  • To propose IFD-YOLO, a novel lightweight detector for onboard infrared sensing systems on UAVs.
  • To enhance accuracy and efficiency in detecting small targets under resource constraints.

Main Methods:

  • Developed IFD-YOLO based on YOLOv11n, incorporating a RepViT backbone for feature extraction.
  • Introduced a C3k2-DyGhost module for dynamic feature fusion and Adaptive Fusion-IoU (AF-IoU) loss for improved bounding-box regression.
  • Validated performance on HIT-UAV and IRSTD-1k datasets.

Main Results:

  • IFD-YOLO achieved a superior balance between accuracy and efficiency compared to YOLOv11n.
  • mAP@50 increased by 4.9% and mAP@50:95 by 3.1%.
  • Model parameters and GFLOPs were reduced by 23% and 21%, respectively.

Conclusions:

  • IFD-YOLO demonstrates strong potential for real-time infrared sensing on resource-constrained UAV platforms.
  • The proposed model effectively addresses challenges in small target detection for aerial surveillance.