Drone Detection using YOLO and SSD A Comparative Study

Pansare, Atharva; Nidhi, S.; Kushwaha, Himanshi; Srivastava, Vedika; Thakur, Nitin; Jamgaonkar, Kanishk; Faiz, Md. Zeeshan

doi:10.1109/iconsip49665.2022.10007489

Cited by 4 publications

(7 citation statements)

References 11 publications

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“…In recent years, many research works have been published to address UAV detection, tracking, and classification problems. The main drone detection technologies are: radar sensors [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], RF sensors [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ], audio sensors [ 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ], and camera sensors using visual UAV characteristics [ 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ]. Based on the above-mentioned sources, the advantages and disadvantages of each drone detection technology are compared in Table 2 .…”

Section: Drone Detection Technologiesmentioning

confidence: 99%

“…The comparative findings proved the efficacy and superiority of the suggested framework, especially for distant and tiny drone detection. A comparative study of drone detection based on one-stage detectors such as YOLO and MobileNetSSDv2 was presented in [ 66 ]. Two open-source datasets were used for drone classification.…”

Section: Drone Detection Technologiesmentioning

confidence: 99%

“…The main steps of the image acquisition process are: energy is reflected from the UAV, the reflected energy is focused by an optical system, and a camera sensor measures the amount of energy.Related works on drone object detection based on visual data can be divided into two major categories depending on whether the authors employed a deep learning model or dealt with feature extraction using traditional handcrafted features. However, recent studies on visual-based drone detection and identification have mostly employed learned features or deep learning models[61][62][63][64][65][66][67][68][69][70][71]. Deep learning for object detection with convolutional neural networks (CNNs) has two approaches: one-stage detection and two-stage detection.…”

mentioning

confidence: 99%

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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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Section: Drone Detection Technologiesmentioning

confidence: 99%

Section: Drone Detection Technologiesmentioning

confidence: 99%

mentioning

confidence: 99%

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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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“…In intelligent transportation systems (ITS), one application of collective intelligence is platooning, which significantly reduces fuel consumption and provides a smooth traffic flow [7]. In drone systems, collective intelligence is used to establish drone swarming when performing certain goals, such as formation control [8], espionage [9], or defense mechanisms [10]. Moreover, collective intelligence is widely used in robotics applications to create multi-agent systems, in which robots perform collaborative actions to accomplish a certain mission [11].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Optical Camera Communication Performance for Collaborative Communication Using Positioning Information

Utama,

Sitanggang,

Nasution

et al. 2024

IEEE Access

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Optical camera communication (OCC) has emerged as a promising alternative technology for radio frequency (RF)-based communication systems. However, existing OCC approaches only consider transmitting data through broadcasting, without any ability for point-to-point communication. In this paper, a new modulation scheme termed distance color-coded on-off keying (DCC-OOK) is proposed that uses distance information retrieved from relative localization. The proposed method allows OCC to perform pointto-point communication and facilitates bidirectional communication. An OCC system that uses multi-sensor fusion of a camera and LiDAR is considered, in which calibration of the sensors and LED array segmentation for performing relative localization between the OCC transmitter and the OCC receiver are explained. The results indicate that both point-to-point and bidirectional communication were achieved with the proposed OCC system. During implementation, the distance between transmitter and receiver was varied between 1.0 and 4.0 m, and the system demonstrated a maximum data rate of 38.4 kbps with a lowest BER of 0.03.

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“…Machines can make real-time decisions based on the facts at hand. The ability of a UAV to fly autonomously and intelligently can be significantly enhanced by integrating CNN into the onboard guiding systems [24,25].…”

mentioning

confidence: 99%

Vision-Based Support for the Detection and Recognition of Drones with Small Radar Cross Sections

Abdelsamad,

Abdelteef,

Elsheikh

et al. 2023

Electronics

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Drones are increasingly vital in numerous fields, such as commerce, delivery services, and military operations. Therefore, it is essential to develop advanced systems for detecting and recognizing drones to ensure the safety and security of airspace. This paper aimed to develop a robust solution for detecting and recognizing drones and birds in airspace by combining a radar system and a visual imaging system, and contributed to this effort by demonstrating the potential of combining the two systems for drone detection and recognition. The results showed that this approach was highly effective, with a high overall precision and accuracy of 88.82% and 71.43%, respectively, and the high F1 score of 76.27% indicates that the proposed combination approach has great effectiveness in the performance. The outcome of this study has significant practical implications for developing more advanced and effective drone and bird detection systems. The proposed algorithm is benchmarked with other related works, which show acceptable performance compared with other counterparts.

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Drone Detection using YOLO and SSD A Comparative Study

Cited by 4 publications

References 11 publications

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

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

Enhancing Optical Camera Communication Performance for Collaborative Communication Using Positioning Information

Vision-Based Support for the Detection and Recognition of Drones with Small Radar Cross Sections

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