卷积神经网络在无人机声音分类中的应用

Donghyun Lim, HeonGyeom Kim, SangGi Hong, Sanghee Lee, GaYoung Kim, Austin Snail, Lucy Gotwals, John C. Gallagher
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引用次数: 7

摘要

在这项工作中,我们分析了一个简单的神经网络的有效性,通过声音确定小型无人驾驶车辆是否携带潜在有害的有效载荷。这项工作的目标是为实时无人机探测系统做出贡献,该系统需要一种评估进入车辆的威胁级别的方法,这些车辆的位置由其他传感器确定。此外,我们在最低成本限制下运作,使执法机构最终能够大规模采用。我们的系统通过向简单的卷积神经网络(CNN)提供声音频谱数据,对携带有效载荷与不携带有效载荷的大疆幻影II无人机进行分类。这些网络,加上一个简单的投票系统,在不违反我们的最小成本约束的情况下,为这个问题提供了99.92%的识别率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Practically Classifying Unmanned Aerial Vehicles Sound Using Convolutional Neural Networks
In this work, we analyze the effectiveness of a simple neural network for the task of determining, by sound, if small unmanned vehicles are carrying potentially harmful payloads The goal of this work is to contribute to a real-time UAV detection system that requires a means of assessing threat level of incoming vehicles whose positions are determined by other sensors. Further, we operated under a minimal cost constraints to enable eventual adoption at scale by law enforcement agencies. Our system classifies payload carrying vs. non-payload carrying DJI Phantom II UAVs by presenting sound spectrum data to a simple Convolutional Neural Networks (CNN). These networks, along with a simple voting system, provided a 99.92% recognition rate for this problem without a need to violate our minimal cost constraint.
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