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YOLO11-RLN: An aerial UAV algorithm for forest fire detection.

Li Gao1, Gaohua Chen1

  • 1School of Electronic Information Engineering, Taiyuan University of Science and Technology, Shanxi Taiyuan, China.

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|August 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces YOLO11-RLN, an improved forest fire detection algorithm for drones, significantly boosting accuracy and reducing false alarms in complex environments.

Keywords:
LTFRepVGGYOLO11forest fire detectionloss functionnano

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Environmental Monitoring

Background:

  • Existing forest fire detection models struggle with drone adaptability, accuracy, and false detection rates.
  • Unmanned aerial vehicle (UAV) based detection requires robust algorithms for complex forest terrains.

Purpose of the Study:

  • To develop a UAV-oriented forest fire detection algorithm overcoming current limitations.
  • To enhance detection accuracy, reduce false positives, and improve drone adaptability for forest fire monitoring.

Main Methods:

  • Proposed YOLO11-RLN algorithm integrating RepVGG backbone for feature extraction.
  • Introduced a novel long fire-line texture fusion (LTF) module for improved fire feature perception.
  • Implemented WIoU loss function and YOLOv8-nano parameterization for enhanced small fire detection and model optimization.

Main Results:

  • YOLO11-RLN demonstrated significant improvements over YOLO11.
  • Achieved 7.338% increase in precision, 5.392% in recall, 7.862% in mAP50, and 7.019% in mAP50-75.
  • Statistical analysis confirmed the robustness and significance of the performance enhancements.

Conclusions:

  • The proposed YOLO11-RLN algorithm offers superior performance for UAV-based forest fire detection.
  • The integration of RepVGG, LTF module, WIoU loss, and nano optimization enhances detection capabilities in challenging environments.