A lightweight deep learning framework for early fire and smoke detection in forest fire monitoring
TL;DR
FRW-YOLOv8, a lightweight YOLOv8-based model for fire and smoke detection in UAV-perspective forest scenes is proposed and FasterNeXt is introduced into the backbone to reduce redundant computation while preserving feature representation.
Abstract
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.