Abstract
In this work, we present a forest fire monitoring system using drones and deep learning. The proposed technique aims to solve the problems of traditional forest fire monitoring techniques, such as blind spots, poor real-time performance, expensive operational costs, and large resource consumption. We use image processing techniques to determine if the frame re-turned by the drone contains fire. This process is accomplished in real time and the resultant information is used to decide if any rescue operation is needed. The method proposed in this work has simple operations, high operating efficiency, and low operating costs. In addition, the proposed technique provides digital ability to monitor the forest fires in real-time effectively. Thus, it can assist in avoiding disasters and greatly reduce labor costs and other costs for forest fire disaster prevention and suppression.
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Acknowledgements
Thanks to Guangdong Academy of Forestry Sciences for providing image acquisition support for our UAV and we also thank Guangdong longyandong forest farm for providing site support for the research.
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Zheng, S., Wang, W., Liu, Z. (2022). Design and Research of Forest Farm Fire Drone Monitoring System Based on Deep Learning. In: Shi, S., Ma, R., Lu, W. (eds) 6GN for Future Wireless Networks. 6GN 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 439. Springer, Cham. https://doi.org/10.1007/978-3-031-04245-4_19
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