基于CNN的化工园区火灾火焰图像识别研究
Study on flame image recognition of chemical industrial park fires based on convolutional neural network
张术琳 1张亚楠 1田超 2严翔 1鲁义 1施式亮1
作者信息
- 1. 湖南科技大学 资源环境与安全工程学院,湖南 湘潭 411201
- 2. 湖南科技大学 资源环境与安全工程学院,湖南 湘潭 411201;重庆大学 资源与安全学院,重庆 400044
- 折叠
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.
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.
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Key words
chemical industrial park/fire flame/image recognition/convolutional neural networks/YOLOv5 algorithm/fire data set引用本文复制引用
基金项目
湖南省重点研发计划资助项目(2022GK2042)
湖南省自然科学基金(2023JJ40292)
湖南省教育厅科学研究项目(22C0240)
湖南科技大学博士后科研基金(E62203)
出版年
2024
NETL
NSTL
万方数据