RANGO: A Novel Deep Learning Approach to Detect Drones Disguising from Video Surveillance Systems

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jin Han, Yun-feng Ren, Alessandro Brighente, Mauro Conti
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引用次数: 0

Abstract

Video surveillance systems provide means to detect the presence of potentially malicious drones in the surroundings of critical infrastructures. In particular, these systems collect images and feed them to a deep-learning classifier able to detect the presence of a drone in the input image. However, current classifiers are not efficient in identifying drones that disguise themselves with the image background, e.g., hiding in front of a tree. Furthermore, video-based detection systems heavily rely on the image’s brightness, where darkness imposes significant challenges in detecting drones. Both these phenomena increase the possibilities for attackers to get close to critical infrastructures without being spotted and hence be able to gather sensitive information or cause physical damages, possibly leading to safety threats.

In this paper, we propose RANGO, a drone detection arithmetic able to detect drones in challenging images where the target is difficult to distinguish from the background. RANGO is based on a deep learning architecture that exploits a Preconditioning Operation (PREP) that highlights the target by the difference between the target gradient and the background gradient. The idea is to highlight features that will be useful for classification. After PREP, RANGO uses multiple convolution kernels to make the final decision on the presence of the drone. We test RANGO on a drone image dataset composed of multiple already existing datasets to which we add samples of birds and planes. We then compare RANGO with multiple currently existing approaches to show its superiority. When tested on images with disguising drones, RANGO attains an increase of \(6.6\% \) mean Average Precision (mAP) compared to YOLOv5 solution. When tested on the conventional dataset, RANGO improves the mAP by approximately \(2.2\% \), thus confirming its effectiveness also in the general scenario.

RANGO:从视频监控系统中检测伪装无人机的新型深度学习方法
视频监控系统为检测关键基础设施周围是否存在潜在恶意无人机提供了手段。特别是,这些系统收集图像并将其输入深度学习分类器,该分类器能够检测输入图像中是否存在无人机。然而,目前的分类器无法有效识别伪装成图像背景的无人机,例如躲在树前的无人机。此外,基于视频的检测系统在很大程度上依赖于图像的亮度,而黑暗环境给检测无人机带来了巨大挑战。这两种现象都增加了攻击者在不被发现的情况下接近关键基础设施的可能性,从而能够收集敏感信息或造成物理破坏,可能导致安全威胁。在本文中,我们提出了一种无人机检测算法 RANGO,它能够在目标与背景难以区分的高难度图像中检测无人机。RANGO 基于深度学习架构,利用预处理操作 (PREP),通过目标梯度与背景梯度之间的差异来突出目标。其目的是突出对分类有用的特征。在 PREP 之后,RANGO 利用多重卷积核对无人机的存在做出最终判断。我们在无人机图像数据集上对 RANGO 进行了测试,该数据集由多个已有数据集组成,我们在其中添加了鸟类和飞机样本。然后,我们将 RANGO 与现有的多种方法进行比较,以显示其优越性。在伪装无人机的图像上进行测试时,RANGO 的平均精度(mAP)比 YOLOv5 解决方案提高了(6.6%)。在传统数据集上进行测试时,RANGO 的 mAP 提高了大约 (2.2%),从而证实了它在一般场景下的有效性。
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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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