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LRM-YOLO: A Lightweight YOLOv10n-Based Model for Forest Fire Smoke Detection in UAV Images
Yong Liu1, Shaochen Jiang1, Yongming Li1
1College of Computer Science and Technology, XinJiang University, Urumqi 830046, China.
Sensors (Basel, Switzerland)
|August 13, 2026
Summary
This study introduces a lightweight forest fire smoke detection model using YOLOv10n for unmanned aerial vehicles (UAVs). The model enhances accuracy and reduces complexity, improving early fire detection capabilities.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Forestry Science
Background:
- Unmanned aerial vehicles (UAVs) are crucial for forest fire monitoring due to their agility and broad observation range.
- Current detection algorithms face challenges in balancing computational cost and performance across varied backgrounds, with limited relevant datasets.
Purpose of the Study:
- To develop a lightweight and efficient forest fire smoke detection model for UAVs.
- To improve the accuracy and reduce the computational complexity of existing smoke detection systems.
Main Methods:
- Proposed a lightweight model based on YOLOv10n, incorporating the RepViTBlock module for enhanced feature extraction.
- Introduced a Lightweight Efficient Convolutional Detection head (LECD) to optimize target recognition and localization.
- Utilized Minimum Point Distance Intersection over Union (MPDIoU) for improved bounding-box regression accuracy.
- Created a new UAV-perspective Forest Fire Smoke (UFFS) dataset.
Main Results:
- The proposed model achieved a 36.7% reduction in parameters and a 42.3% decrease in GFLOPs on the UFFS dataset.
- mAP50 improved by 1.3% and mAP50-95 by 3.7% compared to the baseline.
- Recall increased by 3.2% and precision by 3.6%, demonstrating a better accuracy-complexity trade-off.
Conclusions:
- The developed lightweight model offers a superior balance between detection performance and computational efficiency for UAV-based forest fire smoke detection.
- The new UFFS dataset provides valuable data for training and evaluating models in diverse, real-world scenarios.
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