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This research aims to propose an efficient model for detecting forest smoke using unmanned aerial vehicles.

LM‐YOLO: A Lightweight Multi‐Scale Enhanced Model for Forest Smoke Detection Using Unmanned Aerial Vehicles

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This research aims to propose an efficient model for detecting forest smoke using unmanned aerial vehicles.
Developed a lightweight multi-scale YOLO model named LM-YOLO.
Introduced RFMBlock for enhanced feature representation and a two-path downsampling module.
Utilized Shape-IoU loss for better localization of small smoke areas.
LM-YOLO reduces parameters by 34.5% and computational cost by 30.2% compared to YOLOv12n.
Achieved 92.7% precision on the try7 dataset.
Improved detection precision by 4.7% over baseline methods.

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Randomized trial explores forest smoke detection using a new lightweight model, highlighting improved efficiency and accuracy for drones.

Zhu et al. (2026) studied this question.

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