基于CNN的化工园区火灾火焰图像识别研究

doi:10.16265/j.cnki.issn1003-3033.2024.01.2333

摘要/Abstract

摘要：

为及时发现化工园区火灾事故,降低事故损失,利用卷积神经网络(CNN)建立化工园区火灾实时检测系统。基于CNN-YOLOv5算法训练化工园区火灾数据集和普通火灾数据集,分析对比2个数据集的损失值、召回率、精度和类别平均精度。其中,化工园区火灾数据集的损失值和召回率略低,但精度和类别平均精度高于普通火灾数据集,证明通过CNN检测化工园区火灾的可行性。结果表明:基于火灾检测结果,借助PyQt5程序框架设计化工园区火焰图像识别软件系统,可实现对化工园区火灾火焰图像和视频的识别应用,扩大该方法适用范围。基于CNN的YOLOv5目标检测算法可以实时检测化工园区火灾,其检测方法具有便携性、检测结果具有可靠性,可提高化工园区的安全管理水平。

关键词: 化工园区, 火灾火焰, 图像识别, 卷积神经网络(CNN), YOLOv5算法, 火灾数据集

Abstract:

In order to discover fire accidents in chemical industrial parks in time and reduce accident losses, this study used CNN to establish a real-time fire detection system for chemical industrial parks. Based on CNN, the YOLOv5 algorithm was used to calculate chemical industrial park fire data sets and ordinary fire data sets. The loss value, recall rate, precision and mean average precision of the two data sets were compared. Among them, the loss value and recall rate of the chemical industrial park fire data set are slightly lower, but the precision and mean average precision were higher than that of an ordinary fire data set, which shows the feasibility of detecting fire. In addition, based on fire detection results, this study further designed the flame image recognition software system for the chemical industry park with the help of the PyQt5 program framework, realized the application of fire image and video recognition in the chemical park, and expanded the application scope of the method. The results show that the YOLOv5 target detection algorithm based on convolutional neural network can detect fires in chemical industrial parks in real-time. This detection method is portable,and the results are reliable, which can help improving the safety management level of the chemical industrial park.

Key words: chemical industrial park, fire flame, image recognition, convolutional neural networks, YOLOv5 algorithm, fire data set

中图分类号:

X932爆炸安全与防火、防爆

张术琳, 张亚楠, 田超, 严翔, 鲁义, 施式亮. 基于CNN的化工园区火灾火焰图像识别研究[J]. 中国安全科学学报, 2024, 34(1): 179-186.

ZHANG Shulin, ZHANG Ya'nan, TIAN Chao, YAN Xiang, LU Yi, SHI Shiliang. Study on flame image recognition of chemical industrial park fires based on convolutional neural network[J]. China Safety Science Journal, 2024, 34(1): 179-186.

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