A lightweight deep learning framework for early fire and smoke detection in forest fire monitoring
Efficient early forest fire detection is important for mitigating wildfire spread and related ecological and socio-economic losses. However, vision-based detection remains challenging because fire and smoke targets often exhibit large-scale variation, irregular morphology, low contrast, blurred boundaries, and strong interference from complex forest backgrounds. This study proposes FRW-YOLOv8, a lightweight YOLOv8-based model for fire and smoke detection in UAV-perspective forest scenes. FasterNeXt is introduced into the backbone to reduce redundant computation while preserving feature representation. A reparameterized feature pyramid network, composed of SimConv, transposed convolution, and RepBlock, is designed to enhance cross-scale feature interaction and semantic fusion. Wise-IoU is further adopted to improve bounding-box regression for small and irregular fire-related targets. The model was trained and internally evaluated on the M4SFWD dataset, which provides diverse UAV-perspective forest fire scenes. On M4SFWD, FRW-YOLOv8 improved Fire AP50 and Smoke AP50 by 1.0 and 2.1 percentage points, respectively, while mAP50 and mAP50-95 increased by 1.6 and 1.7 percentage points compared with YOLOv8n. Parameters and FLOPs were reduced by 20.67 and 23.46%, respectively. Although M4SFWD provides diverse UAV-perspective forest fire scenes for model evaluation, its synthetic nature still introduces certain limitations; therefore, two independent real-world external datasets were further evaluated to assess real-scene generalization. FRW-YOLOv8 achieved a Fire AP50 of 88.3% and a Fire AP50-95 of 56.8%. It also achieved a Smoke AP50 of 93.5% and a Smoke AP50-95 of 57.9%, outperforming YOLOv8n by 1.2 and 1.3 percentage points for fire, and by 1.7 and 0.2 percentage points for smoke, respectively. These results indicate an improved trade-off between detection accuracy and model complexity and suggest potential for resource-constrained forest fire monitoring.