摘要
为了提升变电站消防管理科学化水平,实现视频图片火焰特征提取及识别定位。接入视频监控码流数据,构建基于卷积神经网络的火焰识别模型,进行实时识别火焰特征并预警预报。实验表明,该方法能够自动提取火焰特征,有效提高复杂背景下的火焰识别的准确率,具有良好的鲁棒性和泛化能力,在变电站消防管理中有较大应用前景。
In order to improve the fire management of transformer substation,the video and image flame feature extraction and identification are achieved.The stream data of video surveillance is accessed.The flame identification model based on convolution neural network is built.The flame features are real time identified,and early warning and forecast are performed.Experiments showed that,the method can extract the flame feature automatically,and efficiently improve the accuracy of flame identification with complex background,with good robustness and generalization ability,and have great application prospect in the fire management of transformer substation.
作者
李富强
瞿航
徐宁一
朱文超
LI Fu-qiang;QU Hang;XU Ning-yi;ZHU Wen-chao(Ningbo power supply company of State Grid Zhejiang Electric Power Co.,Ltd.,Zhejiang Ningbo 315000,China)
出处
《消防科学与技术》
CAS
北大核心
2020年第12期1770-1772,共3页
Fire Science and Technology
关键词
卷积神经网络
火焰识别
变电站
消防管理
convolution neural network
flame identification
transformer substation
fire management
