Intelligent forest firefighting system based on quadcopter UAV platform and solar power supply system

Yayi Sun; Xianwang Yang; Guowei Dong; Tieshan Xie; Yaqing Kang; Rong Zhang

doi:10.1117/12.3029005

22 May 2024 Intelligent forest firefighting system based on quadcopter UAV platform and solar power supply system

Yayi Sun, Xianwang Yang, Guowei Dong, Tieshan Xie, Yaqing Kang, Rong Zhang

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 1317618 (2024) https://doi.org/10.1117/12.3029005
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

Forest fire seriously affects the safety of the ecological environment, and even threatens people's lives, solving the problem of forest fire is the top priority of forestry work, so how to effectively respond to it has become an important task. The application of UAV technology to forest firefighting and rescue work is of great practical significance in improving rescue efficiency and safety. This study is based on the UAV forest firefighting system, with the theme of inspection aircraft fire intelligence recognition, through the flame smoke recognition technology to inspect the forest. The inspection aircraft uses the Yolov5 algorithm for flame smoke recognition and transmits images of detected open fires to designated personnel and alarms. Using the Pytorch framework and the Yolov5 algorithm, we can easily train a customized dataset and label the flame smoke data. By adjusting the training parameters, we can obtain a highly accurate detection model and finally realize the real-time detection of flame smoke by inspection UAVs.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yayi Sun, Xianwang Yang, Guowei Dong, Tieshan Xie, Yaqing Kang, and Rong Zhang "Intelligent forest firefighting system based on quadcopter UAV platform and solar power supply system", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 1317618 (22 May 2024); https://doi.org/10.1117/12.3029005

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KEYWORDS

Flame

Education and training

Forest fires

Inspection

Detection and tracking algorithms

Data modeling

Unmanned aerial vehicles

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