Fire detection using deep learning methods
Bayegizova A. Abdikerimova G. Kaliyeva S. Shaikhanova A. Shangytbayeva G. Sugurova L. Sugur Z. Saimanova Z.
February 2024Institute of Advanced Engineering and Science
International Journal of Electrical and Computer Engineering
2024#14Issue 1547 - 555 pp.
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.
Classification , Clustering , Deep learning , Machine learning , Natural fire
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Department of Radio Engineering, Electronics and Telecommunications, L. N. Gumilyov Eurasian National University, Astana, Kazakhstan
Department of Information Systems, L. N. Gumilyov Eurasian National University, Astana, Kazakhstan
Department of Computer Science and Information Technology, K. Zhubanov Aktobe Regional University, Aktobe, Kazakhstan
Department of Automation and Telecommunications, M.H. Dulaty Taraz Regional University, Taraz, Kazakhstan
Department of Systems Analysis and Management, L. N. Gumilyov Eurasian National University, Astana, Kazakhstan
Department of Information and Computing System, Abylkas Saginov Karaganda Technical University, Karaganda, Kazakhstan
Department of Radio Engineering
Department of Information Systems
Department of Computer Science and Information Technology
Department of Automation and Telecommunications
Department of Systems Analysis and Management
Department of Information and Computing System
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