安全使用图像处理和深度卷积神经网络

Goutham Reddy Kotapalle, Sachin Kotni
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引用次数: 4

摘要

长期以来,安全一直是每个人都关心的一件大事。私人场所的安全漏洞已经成为每个人都想要消除的威胁。传统的安全系统在检测到安全漏洞时会触发警报。然而,使用图像处理与使用卷积神经网络进行图像识别和分类的深度学习相结合,有助于以增强的方式识别漏洞,从而在很大程度上进一步提高安全性。这是由于它能够使用精确和先进的面部和身体检测算法从图像中提取复杂的特征。机器学习,尤其是深度学习,转型的速度非常快。利用这种技术将现有的系统和模型提升到一个新的水平,将是朝着每个科学和技术领域的进步迈出的一大步。计算机视觉也是如此。这两者耦合并汇集在一起,在安全领域的使用结果实现了很多比想象的可能,这篇论文的目的是做同样的事情。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Security using image processing and deep convolutional neural networks
Safety has, for a long time, been one big thing everyone is concerned about. Security breach of private locations has become a threat that everyone intends to eliminate. The traditional security systems trigger alarms when they detect a security breach. However, the usage of image processing coupled with deep learning using convolutional neural networks for image identification and classification helps in identifying a breach in an enhanced fashion thereby increasing security furthermore to a great extent. This is due to its capability to extract complex features from the images using accurate and advanced face and body detection algorithms. The rate at which machine learning, especially deep learning, is transitioning is very high. The use of such technology in taking the existing systems and models to the next level would be a great step towards advancements in every field of science and technology. The same goes with computer vision. These two coupled and brought together to be used in the field of security results in achieving a lot more than what is imagined to be possible and this paper aims to do the same.
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