基于深度学习的烟雾识别研究  

Smoke recognition based on the depth learning

作　　者：王涛 宫宁生 蒋贵祥 Wang Tao;Gong Ningsheng;Jiang Guixiang(College of Computer Science and Technology,Nanjing Tech University,Nanjing 211816,China)

机构地区：[1]南京工业大学计算机科学与技术学院,江苏南京211816

出　　处：《电子技术应用》2018年第10期131-135,共5页Application of Electronic Technique

基　　金：国家重点基础研究发展计划(973计划)(2005CB321901);软件开发环境国家重点实验室开放课题(BUAA-SKLSDE-09KF-03)

摘　　要：基于Google第二代人工智能学习系统TensorFlow构建神经网络对烟雾图像进行识别检测,通过改进的运动检测算法截取疑似烟雾区域图像,并结合PCA降维算法和Inception Resnet v2网络模型在TensorFlow平台下进行烟雾特征的训练识别。该算法实现了较大范围的火灾实时检测报警,经过实验证明整个检测过程准确地识别了视频流中的烟雾区域,相比于传统烟雾识别方法具有更高的准确率和自适应性,为大范围的火灾烟雾报警提供了一种有效方案。Based on the second generation Artificial Intelligence Learning System(TensorFlow),this paper constructs a neural network to detect smoke images,and uses an improved motion detection algorithm to intercept the image of suspected smoke area.Combining with the PCA dimensionality reduction algorithm,the Inception Resnet V2 network model is trained to recognize the smoke characteristics under the TensorFlow platform.The algorithm realizes a large range of real-time fire detection alarm,and through experiments,it is proved that the whole detection process accurately identifies the smoke region in the video stream,which is more accurate and adaptive than the traditional smoke recognition method,and provides an effective scheme for the large range of fire smoke alarms.

关 键 词：烟雾检测 TensorFlow PCA 卷积神经网络 运动检测 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]