Russian Federation
from 01.01.2013 until now
Russian Federation
Early fire detection is an important factor that can reduce economic and environmental damage and reduce the number of victims. Despite the growing popularity of neural networks as a modern method for solving problems in computer vision, methodological problems often arise in works in this subject area, leading to a decrease or complete devaluation of practical results. This study is devoted to finding such problems among existing works on fire detection. The first section contains a contrast analysis of two works, during which 11 meta-criteria were identified to assess the quality of studies. The second section contains an overview of several works devoted to fire detection in various conditions, both by «classical» methods and using convolutional neural networks. The importance of the correct choice of metrics, the need to choose a model as a process, and a full description of the source data are shown.
computer vision, machine learning, artificial intelligence, object detection, neural networks, convolutional neural networks, Haar Cascades, early fire detection
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