Real-Time Low-Pixel Infrared Human Detection From Unmanned Aerial Vehicles

Ignacio Martinez-Alpiste, Gelayol Golcarenarenji, Qi Wang, J. A. Calero
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引用次数: 4

Abstract

To improve the speed and accuracy in human detection in Search and Rescue (SAR) operations, this paper presents a novel and highly efficient machine learning empowered system by extending the You Only Look Once (YOLO) algorithm, which is designed and deployed on an embedded system. The proposed approach has been evaluated under real-world conditions on a Jetson AGX Xavier platform and the results have shown a well-balanced system in terms of accuracy, speed and portability. Moreover, the system demonstrates its resilience to perform low-pixel human detection on infrared images received from an Unmanned Aerial Vehicle (UAV) at low-light conditions, different altitudes and postures such as sitting, walking and running. The proposed approach has achieved in a constrained environment a total of 89.26% of accuracy and 24.6 FPS, surpassing the barrier of real-time object recognition.
无人机实时低像素红外人体探测
为了提高搜救(SAR)行动中人类检测的速度和准确性,本文通过扩展You Only Look Once (YOLO)算法,提出了一种新型的高效机器学习授权系统,该系统设计并部署在嵌入式系统上。该方法已在Jetson AGX Xavier平台上进行了实际条件下的评估,结果表明该系统在准确性、速度和可移植性方面都达到了良好的平衡。此外,该系统还展示了其在低光条件下、不同高度和坐姿(如坐、走和跑)下对无人机(UAV)接收的红外图像进行低像素人体检测的弹性。该方法在受限环境下实现了89.26%的准确率和24.6 FPS,突破了实时目标识别的障碍。
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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