Deep Learning Based Malicious Drone Detection Using Acoustic and Image Data

Kim, Juann; Lee, Dongwhan; Kim, Youngseo; Shin, Hee-yeon; Heo, Yeeun; Wang, Yaqin; Matson, Eric T.

doi:10.1109/irc55401.2022.00024

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…For optical, around 85 videos totaling 207,663 frames were gathered; of these, 154,089 had a UAV, 5200 had an OFO (other flying object), and 48745 were background frames devoid of any objects. [207] Pitch shifting was employed for data augmentation for acoustic features, and 4220 samples were used for training, 1200 samples were used for validation, and 308 samples were used for testing.…”

Section: Reference Datasets Infomationmentioning

confidence: 99%

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Rahman,

Sejan,

Aziz

et al. 2024

Remote Sensing

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Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital locations such as airports and power plants without authorization, endangering public safety. Because of this, it is critical to accurately and swiftly identify different types of UAVs to prevent their misuse and prevent security issues arising from unauthorized access. In recent years, machine learning (ML) algorithms have shown promise in automatically addressing the aforementioned concerns and providing accurate detection and classification of UAVs across a broad range. This technology is considered highly promising for UAV systems. In this survey, we describe the recent use of various UAV detection and classification technologies based on ML and deep learning (DL) algorithms. Four types of UAV detection and classification technologies based on ML are considered in this survey: radio frequency-based UAV detection, visual data (images/video)-based UAV detection, acoustic/sound-based UAV detection, and radar-based UAV detection. Additionally, this survey report explores hybrid sensor- and reinforcement learning-based UAV detection and classification using ML. Furthermore, we consider method challenges, solutions, and possible future research directions for ML-based UAV detection. Moreover, the dataset information of UAV detection and classification technologies is extensively explored. This investigation holds potential as a study for current UAV detection and classification research, particularly for ML- and DL-based UAV detection approaches.

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Section: Reference Datasets Infomationmentioning

confidence: 99%

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Rahman,

Sejan,

Aziz

et al. 2024

Remote Sensing

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“…The review of the four detection methodologies presented in the previous subsections shows that each of the drone detection modalities has its own set of limitations, and a solid anti-drone system may be supplemented by integrating several modalities. Sensor fusion systems can integrate audio and visual features from acoustic and image sensors [ 77 , 78 , 79 ] or combine radar and visual imaging systems [ 80 , 81 ]; RF and image sensors [ 82 ]; radar, RF, and camera sensors [ 83 ]; optical camera, audio, and radar sensors [ 84 ]; as well as visible, thermal, and audio sensors [ 85 ] to enhance drone identification, tracking, and classification. By integrating the capabilities of several sensory modalities, the method increases the robustness and accuracy of detection systems.…”

Section: Drone Detection Technologiesmentioning

confidence: 99%

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Seidaliyeva,

Ilipbayeva,

Taissariyeva

et al. 2023

Sensors

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The fast development of unmanned aerial vehicles (UAVs), commonly known as drones, has brought a unique set of opportunities and challenges to both the civilian and military sectors. While drones have proven useful in sectors such as delivery, agriculture, and surveillance, their potential for abuse in illegal airspace invasions, privacy breaches, and security risks has increased the demand for improved detection and classification systems. This state-of-the-art review presents a detailed overview of current improvements in drone detection and classification techniques: highlighting novel strategies used to address the rising concerns about UAV activities. We investigate the threats and challenges faced due to drones’ dynamic behavior, size and speed diversity, battery life, etc. Furthermore, we categorize the key detection modalities, including radar, radio frequency (RF), acoustic, and vision-based approaches, and examine their distinct advantages and limitations. The research also discusses the importance of sensor fusion methods and other detection approaches, including wireless fidelity (Wi-Fi), cellular, and Internet of Things (IoT) networks, for improving the accuracy and efficiency of UAV detection and identification.

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“…Additionally, ASI can also be tailored to meet the requirements of a wide range of healthcare applications, such as cardiac auscultation [10], fall detection [11] and hearing-impaired wearable devices [12]. This technology is not only limited to the above-mentioned applications, but is also useful for music genre classification [13], animal and bird species identification [14], [15], robotics [16], drone detection [17], and insect identification [18] as well.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Artificial Intelligence-Based Acoustic Source Identification

Zaheer,

Ahmad,

Habibi

et al. 2023

IEEE Access

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The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions.

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Deep Learning Based Malicious Drone Detection Using Acoustic and Image Data

Cited by 5 publications

References 4 publications

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

A Survey on Artificial Intelligence-Based Acoustic Source Identification

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