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Computer vision-driven forest wildfire and smoke recognition via IoT drone cameras

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Abstract

Forest wildfires often lead to significant casualties and economic losses, making early detection crucial for prevention and control. Internet of Things connected cameras mounted on drone provide wide monitoring coverage and flexibility, while computer vision technology enhances the accuracy and response time of forest wildfire monitoring. However, the small-scale nature of early wildfire targets and the complexity of the forest environment pose significant challenges to accurately and promptly identify fires. To address challenges such as high false-positive rates and inefficiency in existing methods, we propose a Forest Wildfire and Smoke Recognition Network termed FWSRNet. Firstly, we adopt Vision Transformer, which has shown superior performance in recent traditional classification tasks, as the backbone network. Secondly, to enhance the extraction of subtle differential features, we introduce a self-attention mechanism to guide the network in selecting discriminative image patches and calculating their relationships. Next, we employ a contrastive feature learning strategy to eliminate redundant information, making the model more discriminative. Finally, we construct a target loss function for model prediction. Under various proportions of training and testing dataset allocations, the model exhibits recognition accuracies of 94.82, 95.05, 94.90, and 94.80% for forest fires. The average accuracy of 94.89% surpasses five comparative models, demonstrating the potential of this method in IoT-enhanced aerial forest fire recognition.

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Availability of data and materials

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This article has been supported by the Jiangsu Province Key R&D Program (Modern Agriculture) Key Project (BE2023352), Key Medical Research Projects of Jiangsu Provincial Health Commission (ZD2022068), National Natural Science Foundation of China (61941113).

Funding

This research is supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province of China [Grant No. KYCX22_1105], and the National Key R &D Program of China [Grant No. 2019YFD1000400].

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Authors and Affiliations

  1. College of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Yupeng Wang, Yongli Wang, Can Xu & Xiaoli Wang

  2. SD steel Rizhao Co., Ltd, Rizhao, 276800, China

    Yong Zhang

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  1. Yupeng Wang
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Correspondence to Yongli Wang.

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Wang, Y., Wang, Y., Xu, C. et al. Computer vision-driven forest wildfire and smoke recognition via IoT drone cameras. Wireless Netw (2024). https://doi.org/10.1007/s11276-024-03718-0

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