Natural gas station flame detection based on YOLO v5 target detection algorithm

Wei He; Giren Qian; Jianzhong Fu; Xin Yi; Jian Guo; Jianhui He; Cuicui Li; Bingyuan Hong; Baikang Zhu

doi:10.1117/12.2671089

28 April 2023 Natural gas station flame detection based on YOLO v5 target detection algorithm

Wei He, Giren Qian, Jianzhong Fu, Xin Yi, Jian Guo, Jianhui He, Cuicui Li, Bingyuan Hong, Baikang Zhu

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 1261056 (2023) https://doi.org/10.1117/12.2671089
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

Fire is a great danger to the society and economy as well as human life safety, and a small spark can lead to a serious fire. In order to improve the reliability of natural gas station fires warning system, this paper employ YOLO v5 target detection algorithm to investigate the detection of station flames. Firstly, 5488 official datasets and flame images searched for the Internet are collected as training sets and labeled. Secondly, the labeled images are used to train the YOLOv5 network model under Linux operating platform to get the appropriate weighting coefficients to minimize the value of the model loss function. Finally, the trained model is used to identify and detect the flames in the actual natural gas station site environment. The experimental results found that the YOLOv5 algorithm model can achieve real-time supervision of flames, with recognition average precision up to 89.2, fast flame detection and high recognition sensitivity. This work helps to facilitate the realization of real-time monitoring of flames and other hazardous factors of natural gas stations and improve station safety.

Citation Download Citation

Wei He, Giren Qian, Jianzhong Fu, Xin Yi, Jian Guo, Jianhui He, Cuicui Li, Bingyuan Hong, and Baikang Zhu "Natural gas station flame detection based on YOLO v5 target detection algorithm", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 1261056 (28 April 2023); https://doi.org/10.1117/12.2671089

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KEYWORDS

Flame

Detection and tracking algorithms

Target detection

Education and training

Flame detectors

Fire

Target recognition

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