Long range thermal image object recognition for perimeter security

Roxana Mihaescu, S. Carata, Mihai Chindea, M. Ghenescu
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Abstract

Nowadays, the field of long-range thermal image processing is increasingly popular. Although this technology has several advantages in terms of security and monitoring, there are not enough autonomous algorithms for processing these images. In this paper, we aim to address two of the main problems in this field: lack of long-range thermal databases and shortage of autonomous monitoring systems. Firstly, we are introducing a new proprietary database with long-range thermal images. Further, we present a thermal detection algorithm, capable of running on real-time monitoring systems. This algorithm is based on a general-purpose DNN (Deep Neural Network) object detector - the YOLO (You Only Look Once) model. Thus, with minimal computing and hardware resources, this article aims to bring a plus in the field of long-range thermal Image processing.
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用于周边安全的远程热图像目标识别
目前,远程热图像处理领域日益受到重视。尽管该技术在安全性和监控方面具有一些优势,但目前还没有足够的自主算法来处理这些图像。在本文中,我们旨在解决这一领域的两个主要问题:缺乏远程热数据库和缺乏自主监测系统。首先,我们引入了一个新的专有的远程热图像数据库。此外,我们提出了一种能够在实时监控系统上运行的热检测算法。该算法基于通用的DNN(深度神经网络)对象检测器- YOLO(你只看一次)模型。因此,在最小的计算和硬件资源下,本文旨在为远程热图像处理领域带来优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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