FCOS Based Human Detection System Using Thermal Imaging for UAV Based Surveillance Applications

Prashanth Kannadaguli
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引用次数: 2

Abstract

This work is related to building a Human Detection system based on Fully Connected One Shot (FCOS). It is one of the most recent Deep Learning approaches primitively built using single shot detection proposal. Unlike the double stage region-based object detection schemes this technique do not follow semantic segmentation, it does not undergo loss of the object information such as disappearance of the gradients and it does not require pre-defined anchors. This technique comprises strong feature extractors and reinforce multi scale object detection and it is very quick in the multithreaded GPU environments. Since our fundamental research is concentrated on object classification related to Unmanned Aerial Vehicle (UAV) applications, as a first step we choose to detect the humans from thermal dataset. Therefore, we used thermal images and videos possessed from thermal cameras of UAV lm to 50m above ground level as our dataset in building the model and testing. The FCOS extracts the features of an object using its efficient per-pixel fashion. Finally, the performance analysis of these model in terms of mean Average Precision (mAP) indicates that the modelling using FCOS performs in a promising way and it can be used in automatic human detection systems.
基于FCOS的热成像人体检测系统在无人机监视中的应用
本文的工作是建立一个基于全连接单镜头(FCOS)的人体检测系统。它是最新的深度学习方法之一,主要使用单镜头检测方案构建。与基于区域的双阶段目标检测方案不同,该技术不遵循语义分割,不会丢失目标信息,如梯度消失,也不需要预定义的锚点。该技术包括强大的特征提取器和增强的多尺度目标检测,在多线程GPU环境下速度非常快。由于我们的基础研究集中在与无人机(UAV)应用相关的目标分类上,作为第一步,我们选择从热数据集中检测人类。因此,在构建模型和测试过程中,我们使用了距地面50米以上的无人机热像仪的热图像和视频作为我们的数据集。FCOS利用其高效的逐像素方式提取对象的特征。最后,从平均精度(mAP)的角度对这些模型进行了性能分析,表明FCOS建模具有良好的应用前景,可用于人体自动检测系统。
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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