Fire detection algorithm of infrared thermal imaging in petrochemical area based on improved YOLOv4-tiny framework and time-domain feature analysis

Abstract

Oil is one of the most important energy supplies for economic development. In recent years, the fire safety problems of petrochemical enterprises have become prominent, with serious casualties and property losses. The continuously monitoring of key areas through the low-cost and intelligent infrared thermal imaging video monitoring system has important engineering application significance for the improvement of petrochemical site safety problems. According to the characteristics of infrared thermal imaging fire target, this paper proposes a method of deep neural network combined with time-domain feature analysis to realize fire detection. Firstly, high thermal pixels are extracted from the infrared image, and the gray-scale image is converted into a binary gray-scale image. Based on the YOLOv4 tiny framework, multi-level channel prediction and attention mechanism are added to detect the fire candidate target of the binary image, Finally, the candidate target is finally determined by analyzing the time-domain characteristics. Compared with the traditional temperature threshold judgment infrared temperature measurement fire alarm system, it can achieve high detection rate and effectively reduce the false alarm rate of the system. The intelligent security monitoring system in Petrochemical area designed in this paper has been applied in practical engineering, and the fire detection effect is good, which realizes the requirements of low power consumption, low cost and high reliability of the security monitoring system in Petrochemical area based on infrared thermal imaging.