Audio Based Drone Detection and Identification using Deep Learning

Al-Emadi, Sara; Al-Ali, Abdulla; Mohammad, Amr; Al‐Ali, Abdulaziz

doi:10.1109/iwcmc.2019.8766732

Cited by 94 publications

(78 citation statements)

References 10 publications

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“…UAV sounds can be recorded in control environments to make datasets that can further be augmented using noisy environments to simulate real environments. With these augmented datasets, various models based on support vector machines (SVM) and neural networks can be developed for UAV detection [102] , [103] , [104] . Besides these, fundamental audio processing approaches such as audio fingerprinting may also be used [105] .…”

Section: Uav Monitoringmentioning

confidence: 99%

A Comprehensive Review of Unmanned Aerial Vehicle Attacks and Neutralization Techniques

et al. 2021

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Unmanned Aerial Vehicles (UAV) have revolution- ized the aircraft industry in this decade. UAVs are now capable of carrying out remote sensing, remote monitoring, courier delivery, and a lot more. A lot of research is happening on making UAVs more robust using energy harvesting techniques to have a better battery lifetime, network performance and to secure against attackers. UAV networks are many times used for unmanned missions. There have been many attacks on civilian, military, and industrial targets that were carried out using remotely controlled or automated UAVs. This continued misuse has led to research in preventing unauthorized UAVs from causing damage to life and property. In this paper, we present a literature review of UAVs, UAV attacks, and their prevention using anti-UAV techniques. We first discuss the different types of UAVs, the regulatory laws for UAV activities, their use cases, recreational, and military UAV incidents. After understanding their operation, various techniques for monitoring and preventing UAV attacks are described along with case studies.

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Section: Uav Monitoringmentioning

confidence: 99%

A Comprehensive Review of Unmanned Aerial Vehicle Attacks and Neutralization Techniques

et al. 2021

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“…It has made a remarkable impact in computer vision performance previously unattainable on many tasks such as image classification and object detection. Deep learning is applied in research concerning graphical modeling, pattern recognition, signal processing [1], computer vision [2], speech recognition [3], language recognition [4,5], audio recognition [6], and face recognition (FR) [7]. In biometrics, deep learning can be used to represent the unique biometric data and make improvements in the performance of many authentication and recognition systems.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network-Based Approaches for Face Recognition

2019

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Face recognition (FR) is defined as the process through which people are identified using facial images. This technology is applied broadly in biometrics, security information, accessing controlled areas, keeping of the law by different enforcement bodies, smart cards, and surveillance technology. The facial recognition system is built using two steps. The first step is a process through which the facial features are picked up or extracted, and the second step is pattern classification. Deep learning, specifically the convolutional neural network (CNN), has recently made commendable progress in FR technology. This paper investigates the performance of the pre-trained CNN with multi-class support vector machine (SVM) classifier and the performance of transfer learning using the AlexNet model to perform classification. The study considers CNN architecture, which has so far recorded the best outcome in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) in the past years, more specifically, AlexNet and ResNet-50. In order to determine performance optimization of the CNN algorithm, recognition accuracy was used as a determinant. Improved classification rates were seen in the comprehensive experiments that were completed on the various datasets of ORL, GTAV face, Georgia Tech face, labelled faces in the wild (LFW), frontalized labeled faces in the wild (F_LFW), YouTube face, and FEI faces. The result showed that our model achieved a higher accuracy compared to most of the state-of-the-art models. An accuracy range of 94% to 100% for models with all databases was obtained. Also, this was obtained with an improvement in recognition accuracy up to 39%.

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“…For example, drones are used by some people to eavesdrop on people's wireless communication data from long distances [4]. Therefore, relevant authorities have considered the safety and privacy issues involved in drones and efficient identification and detection of drone signals need to be adopted [5].…”

Section: Introductionmentioning

confidence: 99%

Deep Complex-valued Convolutional Neural Network for Drone Recognition Based on RF Fingerprinting

Gu¹,

Qing²,

Wang³

et al. 2020

Preprint

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Drones-aided ubiquitous applications play more and more important roles in our daily life. Accurate recognition of drones is required in aviation management due to their potential risks and even disasters. Radio frequency (RF) fingerprintingbased recognition technology based on deep learning is considered as one of the effective approaches to extract hidden abstract features from RF data of drones. Existing deep learning-based methods are either a high computational burden or low accuracy. In this paper, we propose a deep complex-valued convolutional neural network (DC-CNN) method based on RF fingerprinting for recognizing different drones. Compared with existing recognition methods, the DC-CNN method has the advantages of high recognition accuracy, fast running time and small network complexity. Nine algorithm models and two datasets are used to represent the superior performance of our system. Experimental results show that our proposed DC-CNN can achieve recognition accuracy of 99.5% and 74.1% respectively on 4 and 8 classes of RF drone datasets.

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Audio Based Drone Detection and Identification using Deep Learning

Cited by 94 publications

References 10 publications

A Comprehensive Review of Unmanned Aerial Vehicle Attacks and Neutralization Techniques

A Comprehensive Review of Unmanned Aerial Vehicle Attacks and Neutralization Techniques

Deep Convolutional Neural Network-Based Approaches for Face Recognition

Deep Complex-valued Convolutional Neural Network for Drone Recognition Based on RF Fingerprinting

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