Intelligent on-Board Forest Fire Search System
DOI:
https://doi.org/10.18372/1990-5548.74.17290Keywords:
fire detection, convolutional neural networks, unmanned aerial vehicles, YOLO, R-CNN, single shot MultiBox detector, classifireAbstract
The paper analyzes the situation with forest fires in Ukraine. It is shown that the situation is deteriorating every year. For forest fire monitoring it is substantiated the need of the integrated use of data from satellites and unmanned aerial vehicles. It has been shown that early detection of a fire before it becomes a disaster is critical to preventing catastrophic fires and saving lives and property. A fire detection approach based on the use of computer vision methods that can work with a non-stationary camera installed on board the unmanned aerial vehicle is substantiated. An approach for detecting a "spot" of fire using convolutional neural networks is proposed. In our task of detecting a forest fire using an unmanned aerial vehicle, tracking based on detection is chosen as the model initialization method, when objects are first detected using the detection method and then linked into tracks (association). The Yolov4-tiny architecture was chosen as the architecture of the neural network detector, which provides high accuracy and speed of binary classification.
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