Abstract:
Fire detection is an important task in the field of safety and emergency
prevention. In recent years, deep learning methods have shown high efficiency
in solving various computer vision problems, including detecting objects in
images. In this paper, monitoring wildfires was considered, which allows you
to quickly respond to them and prevent their spread using deep learning
methods. For the experiment, images from the satellite and images from the
FireWatch sensor were taken as initial data. In this work, the deep learning
algorithms you only look once (YOLO), convolutional neural network
(CNN), and fast recurrent neural network (FastRNN) were considered, which
makes it possible to determine the accuracy of a natural fire. As a result of the
experiments, an automated fire recognition algorithm using YOLOv4 deep
learning methods was created. It is expected that the results of the study will
show that deep learning methods can be successfully applied to detect fire in
images. This may lead to the development of automated monitoring systems
capable of quickly and reliably detecting fire situations, which will help
improve safety and reduce the risk of fires.
