Research on Intelligent Analysis Technology of Power Monitoring Video Data Based on Convolutional Neural Network

Wang Xiaoyan, Chai Pei, Li Rui, Yang Bo, Nie Wenzhao
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Abstract

With the rapid increase of on-site monitoring objects and technological development, the power on-site image monitoring data has increased geometrically. For security managers, it is necessary to efficiently and quickly identify valid data and conduct intelligent early warning. Therefore, this paper proposes an intelligent analysis method of power field surveillance video data based on convolutional neural network. Firstly, the original surveillance video material is extracted. Secondly, the video cloud storage platform is used for storage and data cleaning. At the same time, the hidden danger behaviors are classified and analyzed and marked. Finally, the proposed method is used for feature extraction, model training and field verification to realize intelligent early warning analysis of video big data. The field application effect shows that the method proposed in this paper can realize the functions of video image data feature extraction, model training, and improve the effect of image processing. Eventually, it can meet the needs of on-site video data intelligent analysis and has a certain promotion prospect.
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基于卷积神经网络的电力监控视频数据智能分析技术研究
随着现场监控对象的快速增加和技术的发展,电力现场图像监控数据呈几何级增长。对于安全管理人员来说,高效、快速地识别有效数据并进行智能预警是非常必要的。为此,本文提出了一种基于卷积神经网络的电力场监控视频数据智能分析方法。首先,提取原始监控视频资料。其次,利用视频云存储平台进行存储和数据清洗。同时对隐患行为进行分类分析和标记。最后,将提出的方法用于特征提取、模型训练和现场验证,实现视频大数据的智能预警分析。现场应用效果表明,本文提出的方法能够实现视频图像数据特征提取、模型训练等功能,提高图像处理效果。最终能够满足现场视频数据智能分析的需求,具有一定的推广前景。
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