Flame smoke detection algorithm based on YOLOv5 in petrochemical plant  

作　　者：Yueting Yang Shaolin Hu Ye Ke Runguan Zhou 

机构地区：[1]Guangdong University of Petrochemical Technology,Maoming,China and Jilin Institute of Chemical Technology,jilin,China [2]Automation School,Guangdong University of Petrochemical Technology,Maoming,China

出　　处：《International Journal of Intelligent Computing and Cybernetics》2023年第3期502-519,共18页智能计算与控制论国际期刊（英文）

基　　金：This work was supported by National Natural Science Foundation of China(61973094).

摘　　要：Purpose–Fire smoke detection in petrochemical plant can prevent fire and ensure production safety and life safety.The purpose of this paper is to solve the problem of missed detection and false detection in flame smoke detection under complex factory background.Design/methodology/approach–This paper presents a flame smoke detection algorithm based on YOLOv5.The target regression loss function(CIoU)is used to improve the missed detection and false detection in target detection and improve the model detection performance.The improved activation function avoids gradient disappearance to maintain high real-time performance of the algorithm.Data enhancement technology is used to enhance the ability of the network to extract features and improve the accuracy of the model for small target detection.Findings–Based on the actual situation of flame smoke,the loss function and activation function of YOLOv5 model are improved.Based on the improved YOLOv5 model,a flame smoke detection algorithm with generalization performance is established.The improved model is compared with SSD and YOLOv4-tiny.The accuracy of the improved YOLOv5 model can reach 99.5%,which achieves a more accurate detection effect on flame smoke.The improved network model is superior to the existing methods in running time and accuracy.Originality/value–Aiming at the actual particularity of flame smoke detection,an improved flame smoke detection network model based on YOLOv5 is established.The purpose of optimizing the model is achieved by improving the loss function,and the activation function with stronger nonlinear ability is combined to avoid over-fitting of the network.This method is helpful to improve the problems of missed detection and false detection in flame smoke detection and can be further extended to pedestrian target detection and vehicle running recognition.

关 键 词：Flame smoke detection Target recognition YOLOv5 Image detection Deep learning 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]