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

Existing forest smoke detection models face limitations in recognizing small targets, achieving real-time performance, and maintaining high inference efficiency on edge devices. To overcome these challenges, this study proposes a novel lightweight multi-scale You Only Look Once (LM-YOLO) model that enhances detection accuracy and real-time capability while ensuring computational efficiency. The LM-YOLO integrates the backbone feature extraction and multi-scale fusion mechanisms of YOLOv12n. Specifically, we introduce the RFMBlock to enhance the fused representation of shallow and deep features, considering the visual characteristics of forest smoke. Meanwhile, the two-path downsampling module achieves efficient downsampling with minimal spatial information loss. To further improve localization accuracy for small and blurred smoke regions, we employ the Shape-IoU loss function. Extensive experiments on the public try123-v4 forest fire smoke dataset demonstrate that, compared with the baseline YOLOv12n under the same experimental conditions, LM-YOLO reduces the number of parameters by 34.5%, decreases the computational cost by 30.2%, and improves detection precision by 4.7%. In addition, the model achieves 92.7% precision on the public try7 dataset. These results outperform existing methods and highlight the strong adaptability of LM-YOLO for edge deployment, ultimately providing reliable technical support for early forest fire warning using unmanned aerial vehicles.