Real-Time Forest Fire Detection by Ensemble Lightweight YOLOX-L and Defogging Method

Abstract

Forest fires can destroy forest and inflict great damage to the ecosystem. Fortunately, forest fire detection with video has achieved remarkable results in enabling timely and accurate fire warnings. However, the traditional forest fire detection method relies heavily on artificially designed features; CNN-based methods require a large number of parameters. In addition, forest fire detection is easily disturbed by fog. To solve these issues, a lightweight YOLOX-L and defogging algorithm-based forest fire detection method, GXLD, is proposed. GXLD uses the dark channel prior to defog the image to obtain a fog-free image. After the lightweight improvement of YOLOX-L by GhostNet, depth separable convolution, and SENet, we obtain the YOLOX-L-Light and use it to detect the forest fire in the fog-free image. To evaluate the performance of YOLOX-L-Light and GXLD, mean average precision (mAP) was used to evaluate the detection accuracy, and network parameters were used to evaluate the lightweight effect. Experiments on our forest fire dataset show that the number of the parameters of YOLOX-L-Light decreased by 92.6%, and the mAP increased by 1.96%. The mAP of GXLD is 87.47%, which is 2.46% higher than that of YOLOX-L; and the average fps of GXLD is 26.33 when the input image size is 1280 × 720. Even in a foggy environment, the GXLD can detect a forest fire in real time with a high accuracy, target confidence, and target integrity. This research proposes a lightweight forest fire detection method (GXLD) with fog removal. Therefore, GXLD can detect a forest fire with a high accuracy in real time. The proposed GXLD has the advantages of defogging, a high target confidence, and a high target integrity, which makes it more suitable for the development of a modern forest fire video detection system.

Crack Detection in Orthographic Road Images Based on EC-YOLOX Algorithm

“…Through different periods of development, many versions of YOLO have appeared, and YOLOX [33] has a more rational network structure and better robustness compared to YOLOv3 in 2018 and YOLOv5 in 2020. Currently YOLOX in the field of target detection has made a breakthrough through various ways [34][35][36][37]. The whole YOLOX network can be divided into three parts, which are the backbone feature extraction network, strengthen feature extraction network, and the classification and regression part.…”

Deep-APT: Deep Learning based Efficient Accident Prevention Technique in Fogged Environment

“…In 2023, Huang et al, [19] proposed a YOLOX-L and anti-fog algorithm for forest fire detection. To acquire a clear view before the fog lifts, use the dark channel.…”

An Efficient Forest Fire Target Detection Model Based on Improved YOLOv5

“…Such capabilities can greatly diminish response times, potentially protecting lives and property from harm. Effective forest fire detection enables early detection and rapid response to fires, allowing for timely measures to control the spread of fire, protect human lives and property, and mitigate ecological damage and economic losses caused by fires [13][14][15]. Efficient fire detection helps to improve fire warning capabilities, and prompt timely fire response and rescue operations, ensuring that fires are brought under control and extinguished as early as possible, thereby safeguarding the stability and sustainable development of forest ecosystems [16,17].…”