深度超分辨率能增强无人机探测能力吗?

Vasileios Magoulianitis, Dimitrios Ataloglou, A. Dimou, D. Zarpalas, P. Daras
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引用次数: 38

摘要

无人机的普及程度逐年增加,据报道其应用在全球技术市场中占有很大份额。然而,由于无人机也可以用于非法行动,这就提出了需要遇到的各种安全问题。为此,无人机探测系统已经出现,用于探测和进一步预测敌方无人机。一个非常重要的因素是系统的感官可以“看到”即将到来的无人机的最大探测范围。对于那些使用光学相机来探测无人机的系统来说,主要问题是无人机在消失在天空时的准确探测。本研究提出在检测管道中加入超分辨率(SR)技术,以提高其召回能力。在无人机探测器之前利用深度SR模型将图像放大2倍。两个模型都以端到端方式进行训练,充分利用联合优化效果。大量的实验证明了该方法的有效性,其中检测器的召回性能的潜在增益可达32.4%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Does Deep Super-Resolution Enhance UAV Detection?
The popularity of Unmanned Aerial Vehicles (UAVs) is increasing year by year and reportedly their applications hold great shares in global technology market. Yet, since UAVs can be also used for illegal actions, this raises various security issues that needs to be encountered. Towards this end, UAV detection systems have emerged to detect and further anticipate inimical drones. A very significant factor is the maximum detection range in which the system's senses can “see” an upcoming UAV. For those systems that employ optical cameras for detecting UAVs, the main issue is the accurate drone detection when it fades away into sky. This work proposes the incorporation of Super-Resolution (SR) techniques in the detection pipeline, to increase its recall capabilities. A deep SR model is utilized prior to the UAV detector to enlarge the image by a factor of 2. Both models are trained in an end-to-end manner to fully exploit the joint optimization effects. Extensive experiments demonstrate the validity of the proposed method, where potential gains in the detector's recall performance can reach up to 32.4%.
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