An improved wildfire identification method based on Yolov7 and attention mechanism

Qiang Yang; Fan Yu; Gexiang Zhang; Xiaozhao Jin; Dequan Guo; Ping Wang; Guangle Yao; Kai Liang

doi:10.1117/12.3011266

8 November 2023 An improved wildfire identification method based on Yolov7 and attention mechanism

Qiang Yang, Fan Yu, Gexiang Zhang, Xiaozhao Jin, Dequan Guo, Ping Wang, Guangle Yao, Kai Liang

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 1292304 (2023) https://doi.org/10.1117/12.3011266
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

Wildfires, also called forest fires, are a common natural disaster that often occur in forests and are difficult to control. Detecting and suppressing them at an early stage, primarily through monitoring smoke and fires, is crucial in reducing losses. Thanks to the efforts of researchers, wildfire detection technology has advanced significantly, from traditional manual monitoring to target detection, sensor detection, and infrared detection. However, the various detection methods still have some issues, including low accuracy, high costs, slow detection speeds and susceptibility to interference. This paper presents an improved approach to identifying wildfires based on YOLOv7 with CBAM. Our experimental results indicate that our approach achieves a mean Average Precision (mAP) of 95.77%, surpassing VGG-SSD with CBAM by 0.53%, MobileNetv2-SSD with CBAM by 0.37%, and Faster R-CNN with CBAM by 2.1%. Thus, our method offers a highly accurate approach to wildfire identification.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiang Yang, Fan Yu, Gexiang Zhang, Xiaozhao Jin, Dequan Guo, Ping Wang, Guangle Yao, and Kai Liang "An improved wildfire identification method based on Yolov7 and attention mechanism", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 1292304 (8 November 2023); https://doi.org/10.1117/12.3011266

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