A YOLOv3-based Learning Strategy for Real-time UAV-based Forest Fire Detection

Abstract

Forest resources safety is of paramount importance for natural and public security. Forest fire detection methods have been attracted much attention recently, but the performance in terms of comprehensiveness, rapidity, and accuracy is still not satisfactory. A deep learning fire detection algorithm is proposed in this paper, aiming at improving the detection accuracy and efficiency by using the unmanned aerial vehicle (UAV). A large-scale YOLOv3 network is firstly developed which can ensure the detection accuracy. The algorithm is then applied to UAV forest fire detection (UAV-FFD) platform, where the fire images can be captured by the UAV and transmitted to the ground-station in real time. The testing results indicate that the recognition rate of the detection algorithm is about 91%, and the frame rate can reach up to 30 FPS (Frames Per Second). It shows strong potential in real-time application for precision forest fire detection.