A lightweight algorithm capable of accurately identifying forest fires from UAV remote sensing imagery

Abstract

Forest fires often have a devastating effect on the planet’s ecology. Accurate and rapid monitoring of forest fires has therefore become a major focus of current research. Considering that manual monitoring is often inefficient, UAV-based remote sensing fire monitoring algorithms based on deep learning are widely studied and used. In UAV monitoring, the size of the flames is very small and potentially heavily obscured by trees, so the algorithm is limited in the amount of valid information it can extract. If we were to increase the ability of the algorithm to extract valid information simply by increasing the complexity of the algorithm, then the algorithm would run much slower, ultimately reducing the value of the algorithm to the application. To achieve a breakthrough in both algorithm speed and accuracy, this manuscript proposes a two-stage recognition method that combines the novel YOLO algorithm (FireYOLO) with Real-ESRGAN. Firstly, as regards the structure of the FireYOLO algorithm, “the backbone part adopts GhostNet and introduces a dynamic convolutional structure, which im-proves the information extraction capability of the morphologically variable flame while greatly reducing the computational effort; the neck part introduces a novel cross-layer connected, two-branch Feature Pyramid Networks (FPN) structure, which greatly improves the information extraction capability of small targets and reduces the loss in the information transmission process; the head embeds the attention-guided module (ESNet) proposed in this paper, which enhances the attention capability of small targets”. Secondly, the flame region recognized by FireYOLO is input into Real-ESRGAN after a series of cropping and stitching operations to enhance the clarity, and then the enhanced image is recognized for the second time with FireYOLO, and, finally, the recognition result is overwritten back into the original image. Our experiments show that the algorithms in this paper run very well on both PC-based and embedded devices, adapting very well to situations where they are obscured by trees as well as changes in lighting. The overall recognition speed of Jeston Xavier NX is about 20.67 FPS (latency-free real-time inference), which is 21.09% higher than the AP of YOLOv5x, and are one of the best performance fire detection algorithm with excellent application prospects.