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.

Real-time fire detection algorithms running on small embedded devices based on MobileNetV3 and YOLOv4

“…Sathishkumar et al (2023) proposed a learning without forgetting (LwF) method for fire detection algorithms, which addresses the possibility that the detection model may lose its ability to classify the original dataset when applying migration learning thereby greatly reducing the number of steps required to migrate the detection model for learning. Zheng et al (2023) novel algorithm for remote sensing forest fire detection is proposed, which first uses FireYOLO for the initial recognition of the target, then applies the Real-ESRGAN algorithm to the target to improve image clarity, followed by FireYOLO for a second recognition. Each of these algorithms has its own characteristics and solves some of the challenges in fire detection, but it is still a challenging problem to improve the accuracy of the algorithm and its immunity to interference while reducing the number of parameters to a great extent.…”

FuF-Det: An Early Forest Fire Detection Method under Fog

“…To address this, most studies have enhanced the attention and feature fusion modules of the YOLO algorithm to improve its focus on small fire points and reduce the missed detection rate [50][51][52][53][54]. To address the issue of false positives, Zheng et al [18] proposed a preliminary detection-enhancement secondary detection approach to reduce the false positive rate of YOLOv4 in forest fire detection. However, the YOLO target detection algorithm is sensitive to target size.…”

“…Prescribed fire refers to the deliberate ignition of potential fuels in the forest under the conditions of maximum temperature, relative humidity and wind speed at the threshold required for fire to spread [5] in order to reduce the fuel density in the forest and prevent the occurrence of destructive forest fires [6]. For forest fire monitoring, common methods can be categorized into four types: manual patrols, sensor-based monitoring [7][8][9], fire satellite monitoring [10][11][12][13][14], and UAV monitoring [15][16][17][18]. Manual patrols are inefficient, have limited coverage, and expose humans to potential risks in hazardous environments [19].…”

Firefighting Water Jet Trajectory Detection from Unmanned Aerial Vehicle Imagery Using Learnable Prompt Vectors

“…Zheng [ 22 ] proposed a real-time fire detection algorithm to achieve the initial extraction of flame and smoke features while greatly reducing the computational effort of the network structure. To achieve a breakthrough in both algorithm speed and accuracy, Zheng [ 23 ] designed a two-stage recognition method that combines the novel YOLO algorithm with Real-ESRGAN. Zheng [ 24 ] introduced a trainable matrix in the encoder to compute features, reducing computational burden, emphasizing key features, and shortening training time, while also enhancing the encoding block by iteratively updating high- and low-level features, thereby reducing feature computation and remaining compatible with any state-of-the-art transformer decoder.…”