A Lightweight Forest Fire Detection Method Based on UAV Dual-Modal Images

This paper presents a lightweight method for detecting forest fires using dual-modal remote sensing images captured by an unmanned aerial vehicle (UAV). The aim is to achieve efficient fire monitoring on a computationally resource-constrained UAV platform. The proposed detection network is based on the improved YOLOv8, which uses RGB image and thermal image as network input at the same time. A lightweight dual-modal feature fusion module named DFM is designed to effectively combine RGB and thermal features. The existing C2f module in YOLOv8 was replaced by the lightweight module C2f-F, along with the addition of the parameter-free attention module SimAM. This improvement improves the detection performance of the model while minimizing the model parameters. The evaluation experimental results on the FLAME 2 dataset show that the accuracy of the proposed dual-modal forest fire detection method reaches 98.4%, and the model size is only 2.9MB, which achieves a good balance between accuracy and number of parameters compared with other mainstream methods. Additionally, on the iCrest 2-s edge computing device, the detection speed reaches 20.67FPS, further confirming that this lightweight approach satisfies the real-time detection requirements for forest fires.