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.
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Sun et al. (2024) studied this question.
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