Research on Image-based wildfire intelligent detection method

Qiang Yang; Fan Yu; Gexiang Zhang; Dequan Guo; Ping Wang; Guangle Yao

doi:10.1117/12.2667234

1 March 2023 Research on Image-based wildfire intelligent detection method

Qiang Yang, Fan Yu, Gexiang Zhang, Dequan Guo, Ping Wang, Guangle Yao

Proceedings Volume 12588, International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022); 125880Z (2023) https://doi.org/10.1117/12.2667234
Event: International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022), 2022, Chongqing, China

Abstract

Wildfire, also known as forest fire, is fire that usually occur in forests and are difficult to control. If it could be detected and suppressed at an early stage (mainly smoke and flames), it has important meaning for reducing the loss. With the attention of relevant researchers, wildfire detection technology has become more and more advanced, from traditional manual monitoring to traditional target detection to sensor detection and infrared detection, etc. The various detection methods involved still have problems such as slow detection speed, low accuracy, easy interference and high cost. In this paper, SSD, an advanced target detection method, was chosen from deep learning algorithms. Three independent SSD networks are built with VGG16, MobileNet v2, and EfficientNet b3 as the backbone. The experimental results show that the mAP (mean Average Precision) of VGG16-SSD is 95.34%, which is 4.76% higher than MobileNet v2-SSD and 4.53% higher than EfficientNet b3-SSD. Therefore, VGG16-SSD can effectively detect wildfires in the early stages.

Citation Download Citation

Qiang Yang, Fan Yu, Gexiang Zhang, Dequan Guo, Ping Wang, and Guangle Yao "Research on Image-based wildfire intelligent detection method", Proc. SPIE 12588, International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022), 125880Z (1 March 2023); https://doi.org/10.1117/12.2667234

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KEYWORDS

Education and training

Deep learning

Detection and tracking algorithms

Feature extraction

Forest fires

Performance modeling

Target detection

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