A comparison of machine learning and human performance in the real-time acoustic detection of drones
A comparison of machine learning and human performance in the real-time acoustic detection of drones
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发表时间：
2020
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通讯作者：
V. Alaparthy;Sayan Mandal;Mary Cummings
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作者：
V. Alaparthy;Sayan Mandal;Mary Cummings
- 无人机在娱乐和商业应用中的使用大幅增加，预计在不久的将来会激增。随着这种需求的增加，它们对隐私和安全的威胁也在增加。提供违禁品和未经授权的监视是伴随这项技术增长的新风险。监狱和其他商业场所的场所管理者关注公共安全，鉴于其日益紧张的预算，需要具有成本效益的检测解决方案。因此，需要设计一种低成本、易于维护并且不需要昂贵的实时人工监测和监督的无人机检测系统。为此，本文提出了一种低成本的无人机检测系统，该系统采用卷积神经网络（CNN）算法，利用声学特征。从音频签名导出的梅尔频率倒谱系数（MFCC）作为特征被馈送到CNN，然后CNN预测无人机的存在。我们比较现场测试结果与早期的支持向量机（SVM）检测算法。使用CNN减少了假阳性，提高了正确检测率。之前的测试表明，SVM特别容易受到草坪设备和直升机的错误警报的影响，而使用CNN时，这一点得到了显着改善。此外，为了确定这样的系统与人的表现相比有多好，并且还探索将最终用户包括在检测回路中，进行了人的表现实验。在35名参与者的样本中，人类分类准确率为92.47%。这些初步结果清楚地表明，人类非常善于从其他声音中识别无人机的声学特征，并可以增强CNN的性能。
— Usage of drones has increased substantially in both recreation and commercial applications and is projected to proliferate in the near future. As this demand rises, the threat they pose to both privacy and safety also increases. Delivering contraband and unauthorized surveillance are new risks that accompany the growth in this technology. Prisons and other commercial settings where venue managers are concerned about public safety need cost-effective detection solutions in light of their increasingly strained budgets. Hence, there arises a need to design a drone detection system that is low cost, easy to maintain, and without the need for expensive real-time human monitoring and supervision. To this end, this paper presents a low-cost drone detection system, which employs a Convolutional Neural Network (CNN) algorithm, making use of acoustic features. The Mel Frequency Cepstral Co-efficients (MFCC) derived from audio signatures are fed as features to the CNN, which then predicts the presence of a drone. We compare field test results with an earlier Support Vector Machine (SVM) detection algorithm. Using the CNN yielded a decrease in the false positives and an increase in the correct detection rate. Previous tests showed that the SVM was particularly susceptible to false alarms for lawn equipment and helicopters, which were significantly improved when using the CNN. Also, in order to determine how well such a system compared to human performance and also explore including the end-user in the detection loop, a human performance experiment was conducted. With a sample of 35 participants, the human classification accuracy was 92.47%. These preliminary results clearly indicate that humans are very good at identifying drone’s acoustic signatures from other sounds and can augment the CNN’s performance.