基于人工智能和卷积神经网络的下一级安全自动车辆安全系统的临时防护

J. M. John, Noel Philip Isaac, Jerin Thomas, Subin Alexander, B. S. Syamraj
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引用次数: 0

摘要

本文详细介绍了全自动化车辆安全系统,包括车辆模型、车辆检测、驾驶员人脸识别和虚拟助手引导下的停车系统。该系统的核心技术是使用深度卷积神经网络(cnn)序列构建的。该系统对驾驶员和车辆模型进行人脸识别,进行检测,并通过打开护栏门允许进入。这使得更大的组织可以控制和监控车辆交通,并出于安全目的获取用户数据。对于定量分析,我们表明我们的系统优于领先的车辆安全系统。建议的论文项目网站也可在http://www.astound.ga/igns找到。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improvised Guard for Next Level Security Automated Vehicle Security System using Artificial Intelligence and Convolutional Neural Network
This paper details fully automated vehicle security system involving vehicle model, make detection, driver face recognition and parking system guided by a virtual assistant. The core technology of the system is built using a sequence of deep Convolutional Neural Networks (CNNs). This system performs face recognition of the driver and vehicle model, make detection and permit access by opening barrier gate. This allows bigger organizations to control and monitor vehicle traffic as well as gain user data for security purpose. For quantitive analysis, we show that our system outperforms the leading vehicle security system. Proposed paper project website is also available at http://www.astound.ga/igns.
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