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Forest Fire Detection Based on Improved YOLO11
Jialong Gao1, Yanqiao Zhao1, Bowen Chen1
1School of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China.
Sensors (Basel, Switzerland)
|May 27, 2026
Summary
This study introduces an improved YOLOv11 model for efficient forest fire detection. The enhanced model achieves faster speeds and better accuracy, making forest fire detection systems more lightweight and effective.
Area of Science:
- Computer Science
- Artificial Intelligence
- Environmental Monitoring
Background:
- Existing forest fire detection systems face challenges with model size and accuracy.
- Lightweight and accurate detection models are crucial for real-time forest fire monitoring.
Purpose of the Study:
- To develop a refined forest fire detection approach using an improved YOLOv11 architecture.
- To enhance model lightweighting and improve detection accuracy for forest fire targets.
Main Methods:
- Modified the YOLOv11 backbone network by integrating the ShuffleNetV1 module for efficient deployment.
- Incorporated the SPD-Conv convolutional module to improve feature aggregation for large flames and preserve details for small smoke targets.
Main Results:
- Achieved a real-time inference speed of 148.3 FPS.
- Reduced parameter count by 22.5% and GFLOPs by 15.0%.
- Improved mean Average Precision (mAP) by 0.3%.
Conclusions:
- The improved YOLOv11 model successfully achieves lightweight design and enhanced detection accuracy for forest fires.
- This approach offers a more efficient and effective solution for forest fire detection systems.
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