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Journal articles on the topic 'Drone detection tracking and neutralization'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

GONZALEZ JORGE, HIGINIO, ENRIQUE ALDAO PENSADO, GABRIEL FONTELA CARRERA, FERNANDO VEIGA LOPEZ, EDUARDO BALVIS OUTEIRIÑO, and EDUARDO RIOS OTERO. "ANTI-DRONE TECHNOLOGY." DYNA 99, no. 6 (2024): 599–607. http://dx.doi.org/10.52152/d11227.

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This work provides an overview of the growing use of drones in both civilian and military domains, highlighting the associated risks to public safety and privacy. It discusses the development of counter-drone systems to mitigate these risks, focusing on detection, tracking, classification, and neutralization technologies. Various sensors such as passive radar, acoustic sensors, electro-optical and infrared sensors, radiofrequency analyzers, active radar, and LiDAR are detailed, along with algorithms for data fusion. Mitigation strategies include soft kill methods like spoofing and jamming, as

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Marian, Buric, and Cubber Geert De. "Counter Remotely Piloted Aircraft Systems." MTA Review 27, no. 1 (2017): 9–18. https://doi.org/10.5281/zenodo.1115502.

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An effective Counter Remotely Aircraft System is a major objective of many researchers and industries entities. Their activity is strongly impelled by the operational requirements of the Law Enforcement Authorities and naturally follows both the course of the latest terrorist events and technological developments. The designing process of an effective Counter Remotely Aircraft System needs to benefit from a systemic approach, starting from the legal aspects, and ending with the technical ones. From a technical point of view, the system has to work according to the five “kill chain”

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Kim, Joosung, and Inwhee Joe. "Deep Learning-Based Drone Defense System for Autonomous Detection and Mitigation of Balloon-Borne Threats." Electronics 14, no. 8 (2025): 1553. https://doi.org/10.3390/electronics14081553.

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In recent years, balloon-borne threats carrying hazardous or explosive materials have emerged as a novel form of asymmetric terrorism, posing serious challenges to public safety. In response to this evolving threat, this study presents an AI-driven autonomous drone defense system capable of real-time detection, tracking, and neutralization of airborne hazards. The proposed framework integrates state-of-the-art deep learning models, including YOLO (You Only Look Once) for fast and accurate object detection, and convolutional neural networks (CNNs) for X-ray image analysis, enabling precise iden

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Yuan, Yubin, Yiquan Wu, Langyue Zhao, Yaxuan Pang, and Yuqi Liu. "Multiple Object Tracking in Drone Aerial Videos by a Holistic Transformer and Multiple Feature Trajectory Matching Pattern." Drones 8, no. 8 (2024): 349. http://dx.doi.org/10.3390/drones8080349.

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Drone aerial videos have immense potential in surveillance, rescue, agriculture, and urban planning. However, accurately tracking multiple objects in drone aerial videos faces challenges like occlusion, scale variations, and rapid motion. Current joint detection and tracking methods often compromise accuracy. We propose a drone multiple object tracking algorithm based on a holistic transformer and multiple feature trajectory matching pattern to overcome these challenges. The holistic transformer captures local and global interaction information, providing precise detection and appearance featu

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Truong, Xuan Tung. "DEEP LEARNING TECHNIQUE - BASED DRONE DETECTION AND TRACKING." Journal of Military Science and Technology, no. 73 (June 15, 2021): 10–19. http://dx.doi.org/10.54939/1859-1043.j.mst.73.2021.10-19.

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The usage of small drones/UAVs is becoming increasingly important in recent years. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. This paper resolves the problem of detecting small drones in surveillance videos using deep learning algorithms. Single Shot Detector (SSD) object detection algorithm and MobileNet-v2 architecture as the backbone were used for our experiments. The pre-trained mod

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Aouladhadj, Driss, Ettien Kpre, Virginie Deniau, Aymane Kharchouf, Christophe Gransart, and Christophe Gaquière. "Drone Detection and Tracking Using RF Identification Signals." Sensors 23, no. 17 (2023): 7650. http://dx.doi.org/10.3390/s23177650.

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The market for unmanned aerial systems (UASs) has grown considerably worldwide, but their ability to transmit sensitive information poses a threat to public safety. To counter these threats, authorities, and anti-drone organizations are ensuring that UASs comply with regulations, focusing on strategies to mitigate the risks associated with malicious drones. This study presents a technique for detecting drone models using identification (ID) tags in radio frequency (RF) signals, enabling the extraction of real-time telemetry data through the decoding of Drone ID packets. The system, implemented

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Hong, Tao, Qiye Yang, Peng Wang, et al. "Multitarget Real-Time Tracking Algorithm for UAV IoT." Wireless Communications and Mobile Computing 2021 (August 24, 2021): 1–15. http://dx.doi.org/10.1155/2021/9999596.

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Unmanned aerial vehicles (UAVs) have increased the convenience of urban life. Representing the recent rapid development of drone technology, UAVs have been widely used in fifth-generation (5G) cellular networks and the Internet of Things (IoT), such as drone aerial photography, express drone delivery, and drone traffic supervision. However, owing to low altitude and low speed, drones can only limitedly monitor and detect small target objects, resulting in frequent intrusion and collision. Traditional methods of monitoring the safety of drones are mostly expensive and difficult to implement. In

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Rudys, Saulius, Andrius Laučys, Paulius Ragulis, et al. "Hostile UAV Detection and Neutralization Using a UAV System." Drones 6, no. 9 (2022): 250. http://dx.doi.org/10.3390/drones6090250.

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The technologies of Unmanned Aerial Vehicles (UAVs) have seen extremely rapid development in recent years. UAV technologies are being developed much faster than the means of their legislation. There have been many means of UAV detection and neutralization proposed in recent research; nonetheless, all of them have serious disadvantages. The essential problems in the detection of UAVs is the small size of UAVs, weak radio wave reflection, weak radio signal, and sound emitting. The main problem of conventional UAV countermeasures is the short detection and neutralization range. The authors propos

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Panduri, Bharathi, P. K. Abhilash, T. N. P. Madhuri, Venkata Naga Tejaswi Bethapudi, and Muntather Almusawi. "Sustainable Drone Detection using Deep Learning Paradigms." E3S Web of Conferences 529 (2024): 04005. http://dx.doi.org/10.1051/e3sconf/202452904005.

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Drones are becoming more and more common, which has advantages but also concerns. They can be used to support illegal operations like drug trafficking and endanger places that are important to security. While advances in sensor technology haven't produced reliable answers in the literature, current drone detection and neutralization methods frequently require previous detection and categorization. Using radio frequency (RF) signals and a frequency signature-based deep learning model, this work promotes an environmentally friendly and multidisciplinary method of drone identification and categor

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Zbigniew Koruba and Izabela Krzysztofik. "Dynamics and Control of the Gyroscopic Head used for the Laser Illumination of a Ground Target from the Quadcopter Deck." Communications - Scientific letters of the University of Zilina 23, no. 2 (2021): B158—B164. http://dx.doi.org/10.26552/com.c.2021.2.b158-b164.

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In the paper authors investigate dynamics of a controlled quadcopter in terms of the possibility of its use for detection, observation, tracking and laser illuminating of both stationary and moving ground targets in the conditions of impact of random and kinematic excitations. The drone is equipped with a scanning and tracking Gyroscopic Head (GH) coupled with a laser target indicator. The drone is affected by random disturbances in the form of wind gusts or explosions of missiles. Kinematic excitations, such as drone maneuvers and vibrations from engines, act on the GH. This paper focuses mai

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Phartyal, Deepanshu, and Mihir Ghooi. "DETECTION AND TRACKING OF DRONES USING ARTIFICIAL INTELLIGENCE TECHNIQUES - A SURVEY." International Journal of Advanced Research 10, no. 06 (2022): 922–28. http://dx.doi.org/10.21474/ijar01/14971.

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Enormous mechanisms and mediums are being employed to threaten the defense system and civilians. Drone strikes are one of them, which may refer to the unloading of explosions and supervision as such. So, detecting and tracking the Drone could be a viable solution for any organization to tackle the aerial threat challenges and secure the environment from malicious activities. Thus the present research discusses the current paradigm of Drone strikes, challenges and solutions to deal with such security concerns. The present article aims to examine the current status of Drone Detection critically,

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Wang, Chenghang, Xiaochun Shen, Zhaoxiang Zhang, Chengyang Tao, and Yuelei Xu. "Cross-Scene Multi-Object Tracking for Drones: Leveraging Meta-Learning and Onboard Parameters with the New MIDDTD." Drones 9, no. 5 (2025): 341. https://doi.org/10.3390/drones9050341.

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Multi-object tracking (MOT) is a key intermediate task in many practical applications and theoretical fields, facing significant challenges due to complex scenarios, particularly in the context of drone-based air-to-ground military operations. During drone flight, factors such as high-altitude environments, small target proportions, irregular target movement, and frequent occlusions complicate the multi-object tracking task. This paper proposes a cross-scene multi-object tracking (CST) method to address these challenges. Firstly, a lightweight object detection framework is proposed to optimize

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Oh, Chanyoung, Moonsoo Lee, and Chaedeok Lim. "Towards Real-Time On-Drone Pedestrian Tracking in 4K Inputs." Drones 7, no. 10 (2023): 623. http://dx.doi.org/10.3390/drones7100623.

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Over the past several years, significant progress has been made in object tracking, but challenges persist in tracking objects in high-resolution images captured from drones. Such images usually contain very tiny objects, and the movement of the drone causes rapid changes in the scene. In addition, the computing power of mission computers on drones is often insufficient to achieve real-time processing of deep learning-based object tracking. This paper presents a real-time on-drone pedestrian tracker that takes as the input 4K aerial images. The proposed tracker effectively hides the long laten

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Gupta, Arpit. "Simulation and Detection of Small Drones/Suspicious UAVs in Drone Grid." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 5452–58. http://dx.doi.org/10.22214/ijraset.2021.36144.

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Today’s technology is evolving towards autonomous systems and the demand in autonomous drones, cars, robots, etc. has increased drastically in the past years. This project presents a solution for autonomous real-time visual detection and tracking of hostile drones by moving cameras equipped on surveillance drones. The algorithm developed in this project, based on state-of-art machine learning and computer vision methods, succeeds at autonomously detecting and tracking a single drone by moving a camera and can run at real-time. The project can be divided into two main parts: the detection and t

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P, Bhavani, Prasanna P, Dhivagar M, and Velliangiry S. "Unauthorized Drone Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 282–87. http://dx.doi.org/10.22214/ijraset.2024.61496.

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Abstract: The growing concerns over drone misuse in national airspace, including activities like drug smuggling and privacy violations, demand advanced drone detection systems. Traditional methods struggle to accurately identify drones among various airborne objects. YOLO v8, renowned for its efficient single-pass object detection, presents a sophisticated solution. Through training on annotated datasets featuring drones in diverse environments and fine-tuning with aerial imagery, YOLO v8 excels at recognizing drones amidst complex backgrounds. Its multi-scale detection capabilities enable it

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Liu, Junli, Xiaofeng Liu, Qiang Chen, and Shuyun Niu. "A Traffic Parameter Extraction Model Using Small Vehicle Detection and Tracking in Low-Brightness Aerial Images." Sustainability 15, no. 11 (2023): 8505. http://dx.doi.org/10.3390/su15118505.

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It is still a challenge to detect small-size vehicles from a drone perspective, particularly under low-brightness conditions. In this context, a YOLOX-IM-DeepSort model was proposed, which improved the object detection performance in low-brightness conditions accurately and efficiently. At the stage of object detection, this model incorporates the data enhancement algorithm as well as an ultra-lightweight subspace attention module, and optimizes the number of detection heads and the loss function. Then, the ablation experiment was conducted and the analysis results showed that the YOLOX-IM mod

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Boonsongsrikul, Anuparp, and Jirapon Eamsaard. "Real-Time Human Motion Tracking by Tello EDU Drone." Sensors 23, no. 2 (2023): 897. http://dx.doi.org/10.3390/s23020897.

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Human movement tracking is useful in a variety of areas, such as search-and-rescue activities. CCTV and IP cameras are popular as front-end sensors for tracking human motion; however, they are stationary and have limited applicability in hard-to-reach places, such as those where disasters have occurred. Using a drone to discover a person is challenging and requires an innovative approach. In this paper, we aim to present the design and implementation of a human motion tracking method using a Tello EDU drone. The design methodology is carried out in four steps: (1) control panel design; (2) hum

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Dr. Yogesh Suryawanshi. "Motion Capture Autonomous Drone." Journal of Information Systems Engineering and Management 10, no. 29s (2025): 929–33. https://doi.org/10.52783/jisem.v10i29s.4605.

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Introduction: Motion capture technology is vital in industries like sports biomechanics, cinema, Robotics traditional system rely on fixed cameras limited flexibility in dynamic outdoor environments autonomous rule equipped with AI and computer vision of announced mobility, real time tracking of moving subject across diversity. Drones enable resize performing analysis in sports and dynamic film without need for complex setup all the challenges like environmental conditions and hardware limitation remains research in sensor fusion and optimization is improving reliability Drone represent a majo

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K, Ramya. "DRONE POWERED CLASSROOM PRESENCE TRACKER." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32683.

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In today's rapidly evolving educational landscape, the traditional methods of manual attendance tracking in classrooms are proving to be increasingly inefficient and prone to inaccuracies. This project introduces an innovative solution to modernize classroom attendance tracking through the integration of drone technology and advanced computer vision algorithms. By combining autonomous navigation, object detection, data processing, real-time reporting, and user interface functionalities, the system streamlines attendance management processes in educational environments. Utilizing drones equippe

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Mpouziotas, Dimitris, Petros Karvelis, and Chrysostomos Stylios. "Advanced Computer Vision Methods for Tracking Wild Birds from Drone Footage." Drones 8, no. 6 (2024): 259. http://dx.doi.org/10.3390/drones8060259.

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Wildlife conservationists have historically depended on manual methods for the identification and tracking of avian species, to monitor population dynamics and discern potential threats. Nonetheless, many of these techniques present inherent challenges and time constraints. With the advancement in computer vision techniques, automated bird detection and recognition have become possible. This study aimed to further advance the task of detecting wild birds using computer vision methods with drone footage, as well as entirely automating the process of detection and tracking. However, detecting ob

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Shamshad Ali, Vipin Kumar, Gurdiyal Singh, Himanshu Giroh, and Abul Saeed Azad. "Design of a Microstrip Patch Antenna for Drone Detection and Tracking." Metallurgical and Materials Engineering 31, no. 4 (2025): 930–36. https://doi.org/10.63278/1537.

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With drones rapidly proliferating, opportunities — and challenges — have arisen in the area of surveillance, logistics, and security. But, since their usage is increasing, a robust detection and tracking system is needed to mitigate any possible threat. This work studies drone detection and tracking based on microstrip patch antennas. For this application, these antennas are preferred, with their low profile, low-weight structure, and low cost. Theoretical foundation, design methodology, simulation results, and potential applications related to signal processing and system integration for impr

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Jin, Kangjie, Seung-Soo Han, Donghyun Baek, and Han Lim Lee. "Small Drone Detection Using Hybrid Beamforming 24 GHz Fully Integrated CMOS Radar." Drones 9, no. 7 (2025): 453. https://doi.org/10.3390/drones9070453.

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This paper presents a compact 24 GHz radar with a 4-transmit (4Tx) and 4-receive (4Rx) CMOS radar IC, integrated with a 4 × 4 Tx array and four 1 × 4 receive Rx array antennas, optimized for enhancing small drone detection. By employing the hybrid beamforming technique based on analog beamforming on the transmit side and independent four-channel digital reception, the proposed radar achieves high spatial resolution and robust target tracking. The proposed radar features an elevation scan range of ±45° with an azimuth fan-beam half-power beamwidth (HPBW) of 80° for a comprehensive detection fie

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Hansen, Jakob Grimm, and Rui Pimentel de Figueiredo. "Active Object Detection and Tracking Using Gimbal Mechanisms for Autonomous Drone Applications." Drones 8, no. 2 (2024): 55. http://dx.doi.org/10.3390/drones8020055.

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Object recognition, localization, and tracking play a role of primordial importance in computer vision applications. However, it is still an extremely difficult task, particularly in scenarios where objects are attended to using fast-moving UAVs that need to robustly operate in real time. Typically the performance of these vision-based systems is affected by motion blur and geometric distortions, to name but two issues. Gimbal systems are thus essential to compensate for motion blur and ensure visual streams are stable. In this work, we investigate the advantages of active tracking approaches

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Ksendzuk, A. V. "UNMANNED AERIAL VEHICLE DETECTION AND JAMMING RADIO COMPLEX." Issues of radio electronics, no. 3 (March 20, 2018): 19–24. http://dx.doi.org/10.21778/2218-5453-2018-3-19-24.

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Modern threats for anti-missile system and space surveillance and tracking system objects include terroristic and unidentifiable unmanned aerial vehicle. To counter these threats a concept of radar complex for unmanned aerial vehicles jamming and detection proposed. Complex consists of non-radiating radio locator, radio warfare station and global navigation jamming radar. Structure, principle of operation and basic technical characteristics of these systems described. Counter-drone actions algorithm in proposed complex described and analyzed. Results of mom-radiating radar development in JSC M

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Yeom, Seokwon. "Thermal Image Tracking for Search and Rescue Missions with a Drone." Drones 8, no. 2 (2024): 53. http://dx.doi.org/10.3390/drones8020053.

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Infrared thermal imaging is useful for human body recognition for search and rescue (SAR) missions. This paper discusses thermal object tracking for SAR missions with a drone. The entire process consists of object detection and multiple-target tracking. The You-Only-Look-Once (YOLO) detection model is utilized to detect people in thermal videos. Multiple-target tracking is performed via track initialization, maintenance, and termination. Position measurements in two consecutive frames initialize the track. Tracks are maintained using a Kalman filter. A bounding box gating rule is proposed for

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Nyzhnyk, Andrii, Andrii Partyka, and Michal Podpora. "Using the Raft algorithm to coordinate interceptor drones in a UAV detection and neutralization system." Advances in Cyber-Physical Systems 10, no. 1 (2025): 105–10. https://doi.org/10.23939/acps2025.01.105.

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This article explores the use of the Raft consensus algorithm to coordinate interceptor drones in systems designed to detect and neutralize unmanned aerial vehicles (UAVs). The modified Raft algorithm has enabled stable and synchronized drone actions, allowing for autonomous target interception. Modeling and simulation confirmed the system’s fault tolerance and real-time coordination capabilities. In scenarios involving partial communication failures or drone loss, the system has successfully maintained consensus and continued operation. The proposed architecture has used the Rust programming

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Gong, Jiangkun, Deren Li, Jun Yan, Huiping Hu, and Deyong Kong. "Using Classify-While-Scan (CWS) Technology to Enhance Unmanned Air Traffic Management (UTM)." Drones 6, no. 9 (2022): 224. http://dx.doi.org/10.3390/drones6090224.

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Drone detection radar systems have been verified for supporting unmanned air traffic management (UTM). Here, we propose the concept of classify while scan (CWS) technology to improve the detection performance of drone detection radar systems and then to enhance UTM application. The CWS recognizes the radar data of each radar cell in the radar beam using advanced automatic target recognition (ATR) algorithm and then integrates the recognized results into the tracking unit to obtain the real-time situational awareness results of the whole surveillance area. Real X-band radar data collected in a

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Tohma, Eisaku, Kotaro Tadakuma, Hibiki Chinen, Tansuriyavong Suriyon, and Takashi Anezaki. "Wild Animal Detection and Tracking Drone System Using Centerline Extraction." IEEJ Transactions on Industry Applications 141, no. 2 (2021): 155–60. http://dx.doi.org/10.1541/ieejias.141.155.

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Yuan, Yubin, Yiquan Wu, Langyue Zhao, Jinlin Chen, and Qichang Zhao. "DB-Tracker: Multi-Object Tracking for Drone Aerial Video Based on Box-MeMBer and MB-OSNet." Drones 7, no. 10 (2023): 607. http://dx.doi.org/10.3390/drones7100607.

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Drone aerial videos offer a promising future in modern digital media and remote sensing applications, but effectively tracking several objects in these recordings is difficult. Drone aerial footage typically includes complicated sceneries with moving objects, such as people, vehicles, and animals. Complicated scenarios such as large-scale viewing angle shifts and object crossings may occur simultaneously. Random finite sets are mixed in a detection-based tracking framework, taking the object’s location and appearance into account. It maintains the detection box information of the detected obje

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Lin, Yeneng, Mengmeng Wang, Wenzhou Chen, Wang Gao, Lei Li, and Yong Liu. "Multiple Object Tracking of Drone Videos by a Temporal-Association Network with Separated-Tasks Structure." Remote Sensing 14, no. 16 (2022): 3862. http://dx.doi.org/10.3390/rs14163862.

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The task of multi-object tracking via deep learning methods for UAV videos has become an important research direction. However, with some current multiple object tracking methods, the relationship between object detection and tracking is not well handled, and decisions on how to make good use of temporal information can affect tracking performance as well. To improve the performance of multi-object tracking, this paper proposes an improved multiple object tracking model based on FairMOT. The proposed model contains a structure to separate the detection and ReID heads to decrease the influence

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Mpouziotas, Dimitrios, Petros Karvelis, Ioannis Tsoulos, and Chrysostomos Stylios. "Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques." Applied Sciences 13, no. 13 (2023): 7787. http://dx.doi.org/10.3390/app13137787.

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Wildlife conservationists have traditionally relied on manual identification and tracking of bird species to monitor populations and identify potential threats. However, many of these techniques may prove to be time-consuming. With the advancement of computer vision techniques, automated bird detection and recognition have become possible. In this manuscript, we present an application of an object-detection model for identifying and tracking wild bird species in natural environments. We used a dataset of bird images captured in the wild and trained the YOLOv4 model to detect bird species with

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Yang, Shao-Yu, Hsu-Yung Cheng, and Chih-Chang Yu. "Real-Time Object Detection and Tracking for Unmanned Aerial Vehicles Based on Convolutional Neural Networks." Electronics 12, no. 24 (2023): 4928. http://dx.doi.org/10.3390/electronics12244928.

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This paper presents a system applied to unmanned aerial vehicles based on Robot Operating Systems (ROSs). The study addresses the challenges of efficient object detection and real-time target tracking for unmanned aerial vehicles. The system utilizes a pruned YOLOv4 architecture for fast object detection and the SiamMask model for continuous target tracking. A Proportional Integral Derivative (PID) module adjusts the flight attitude, enabling stable target tracking automatically in indoor and outdoor environments. The contributions of this work include exploring the feasibility of pruning exis

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Nishat, Humaira, Shakeel Ahmed, and Devarapally Akanksha. "Drone Detection in Restricted Areas Using Deep Learning." Jurnal Kejuruteraan 37, no. 2 (2025): 609–16. https://doi.org/10.17576/jkukm-2025-37(2)-05.

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As the use of drones becomes more widespread, the corresponding rise in drone-related intrusions poses a growing threat to public safety and privacy. Traditional anti-drone systems typically rely on radio-frequency sensors for drone tracking. This paper investigates the fusion of deep learning-based detection algorithms with surveillance cameras within the framework of radio-frequency anti-drone systems. The primary aim is to assess the efficacy of contemporary models and training methodologies in achieving precise and real-time drone detection. One of the central challenges addressed in this

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Abdelnasser, Heba, Mohammad Heggo, Oscar Pang, Mirko Kovac, and Julie A. McCann. "RaDro: Indoor Drone Tracking Using Millimeter Wave Radar." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, no. 3 (2024): 1–23. http://dx.doi.org/10.1145/3678549.

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Core to drone design is its ability to ascertain its location by utilizing onboard inertial sensors combined with GPS data. However, GPS is not always reachable, especially in challenging environments such as indoors. This paper proposes RaDro; a system that leverages millimeter-waves (mmWave) to precisely localize and track drones in indoor environments. Unlike commonly used alternative technologies, RaDro is cost-effective and can penetrate obstacles, a bonus in non-line-of-sight (NLoS) scenarios, which enhances its reliability for tracking objects in complex environments. It does this witho

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Amala Arokia Nathan, Rakesh John, Indrajit Kurmi, David C. Schedl, and Oliver Bimber. "Through-Foliage Tracking with Airborne Optical Sectioning." Journal of Remote Sensing 2022 (April 22, 2022): 1–10. http://dx.doi.org/10.34133/2022/9812765.

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Detecting and tracking moving targets through foliage is difficult, and for many cases even impossible in regular aerial images and videos. We present an initial light-weight and drone-operated 1D camera array that supports parallel synthetic aperture aerial imaging. Our main finding is that color anomaly detection benefits significantly from image integration when compared to conventional raw images or video frames (on average 97% vs. 42% in precision in our field experiments). We demonstrate that these two contributions can lead to the detection and tracking of moving people through densely

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Junior, Milembolo Miantezila, and Bin Guo. "Sensing spectrum sharing based massive MIMO radar for drone tracking and interception." PLOS ONE 17, no. 5 (2022): e0268834. http://dx.doi.org/10.1371/journal.pone.0268834.

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Radar sensors are becoming crucial for environmental perception in a world with the tremendous growth of unmanned aerial vehicles (UAVs) or drones. When public safety is a concern, the localization of drones are of great significance. However, a drone used for a wrong motive can cause a serious problem for the environment and public safety, given the fact that the dynamic movement of a drone’s emission signal and location tracking is different from existing positioning. This study proposes a safety zone characterized by the presence of N radars sensors with a goal to track and destabilized rog

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Huang, Weicheng, Weijie Li, Liming Yang, Wenqian Zhang, and Li Wang. "Disaster Rescue Drone Based on YOLOv4 Algorithm." Journal of Physics: Conference Series 2850, no. 1 (2024): 012005. http://dx.doi.org/10.1088/1742-6596/2850/1/012005.

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Abstract With the rapid development of artificial intelligence technology, the application of unmanned aerial vehicles (UAV) in disaster relief is becoming more widespread. This article presents a disaster relief UAV based on the YOLOv4 algorithm, aimed at improving the speed and efficiency of emergency response and rescue. The article designs and implements a UAV integrated with the YOLOv4 object detection algorithm, used for real-time identification and location of people within disaster areas and for deploying rescue materials using a mechanical claw. Through experiments and comparative ver

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Shahsavari, Sajad, Mohammed Rabah, Eero Immonen, Mohammad-Hashem Haghbayan, and Juha Plosila. "Remote Run-Time Failure Detection and Recovery Control For Quadcopters." Journal of Integrated Design and Process Science 25, no. 2 (2022): 120–40. http://dx.doi.org/10.3233/jid210017.

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We propose an adaptive run-time failure recovery control system for quadcopter drones, based on remote real-time processing of measurement data streams. Particularly, the measured RPM values of the quadcopter motors are transmitted to a remote machine which hosts failure detection algorithms and performs recovery procedure. The proposed control system consists of three distinct parts: (1) A set of computationally simple PID controllers locally onboard the drone, (2) a set of computationally more demanding remotely hosted algorithms for real-time drone state detection, and (3) a digital twin co

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Thakre, Prof Sumit, Mr Rahul Gavade, Ms Sarita Shinde, Mr Varun Gujar, and Mr Omkar Kamble. "Autonomous Delivery Drone." International Scientific Journal of Engineering and Management 04, no. 02 (2025): 1–7. https://doi.org/10.55041/isjem02264.

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The rapid advancement of drone technology has opened new possibilities for efficient and autonomous delivery systems. This project focuses on developing an autonomous delivery drone system using the E88 Pro Max and KK2.1.5 flight controller. The goal is to design a reliable, cost-effective, and intelligent drone capable of delivering packages with minimal human intervention. The project involves integrating GPS navigation, obstacle avoidance, and AI-based path planning to ensure precise and safe deliveries. The E88 Pro Max provides a lightweight and efficient platform, while the KK2.1.5 contro

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Zhao, Qiang, and Limei Peng. "High-altitude Multi-object Detection and Tracking based on Drone Videos." Journal of Networking and Network Applications 2, no. 1 (2022): 36–42. http://dx.doi.org/10.33969/j-nana.2022.020103.

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Drone videos have more extensive shooting ranges, more angles, and no geographical limitations. Thus the object detection algorithm based on drone videos is increasingly playing a role in various fields, such as military surveillance, space remote sensing, smart city, disaster monitoring scenes, etc. Compared to low-altitude object detection and tracking (LA-ODT), high-altitude object detection and tracking (HA-ODT) are receiving increasing attention, especially in modern cities with massive high buildings, because of their higher flying h eight, w ider v iewing a ngle, a nd t he a bility t o

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Lee, Min-Hyuck, and Seokwon Yeom. "Multiple target detection and tracking on urban roads with a drone." Journal of Intelligent & Fuzzy Systems 35, no. 6 (2018): 6071–78. http://dx.doi.org/10.3233/jifs-169847.

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Zalesky, B. A., V. A. Ivanyukovich, K. V. Reer, and D. A. Starikovich. "Comparative analysis of object tracking algorithms." Informatics 22, no. 1 (2025): 66–72. https://doi.org/10.37661/1816-0301-2025-22-1-66-72.

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Objectives. The article presents the results of calculation and comparative analysis of the characteristics of the algorithm proposed by the authors in [1] for tracking an object captured by a video camera, when solving the urgent task of automatic detection and tracking of drones. Two algorithms were selected for comparative analysis, one of which is the currently known open source ByteTrack tracker, and the other is a simple tracker based on the use of the neural network, correlation comparison together with Kalman filter. The first tracker was chosen because it can be implemented in C++ wit

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Harasyn, Madison L., Wayne S. Chan, Emma L. Ausen, and David G. Barber. "Detection and tracking of belugas, kayaks and motorized boats in drone video using deep learning." Drone Systems and Applications 10, no. 1 (2022): 77–96. http://dx.doi.org/10.1139/juvs-2021-0024.

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Aerial imagery surveys are commonly used in marine mammal research to determine population size, distribution and habitat use. Analysis of aerial photos involves hours of manually identifying individuals present in each image and converting raw counts into useable biological statistics. Our research proposes the use of deep learning algorithms to increase the efficiency of the marine mammal research workflow. To test the feasibility of this proposal, the existing YOLOv4 convolutional neural network model was trained to detect belugas, kayaks and motorized boats in oblique drone imagery, collec

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Wu, Dongli. "Object detection and tracking for drones: A system design using dynamic visual SLAM." Applied and Computational Engineering 81, no. 1 (2024): 71–82. http://dx.doi.org/10.54254/2755-2721/81/20241013.

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Abstract. Drones can play a quite crucial role in many walks of life today. Enhancing the visual perception ability of drones is crucial to their intelligence level. Among them, it is necessary to focus on strengthening the detection, tracking and mapping capabilities of drones for dynamic objects. However, the existing visual SLAM systems carried by drones do not perform well in dynamic environments. This project designs a monocular visual SLAM system specifically for drones, aiming to achieve efficient three-dimensional mapping and target tracking, surpassing the limitations of simple static

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Niwa, Hideyuki, and Yuya Sawai. "Verification of the Detection Performance of Drone Radio Telemetry for Tracking the Movement of Frogs." Drones 5, no. 4 (2021): 139. http://dx.doi.org/10.3390/drones5040139.

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Elucidating the various behavioral and ecological uses of animal habitats is the basis for the conservation and management of animal species. Therefore, tracking the movement of animals is necessary. Biotelemetry is used for tracking the movement of animals. By mounting a radio telemetry receiver and antenna on a drone, the time and labor required for surveying animals can be reduced. In addition, it is easy to track difficult-to-reach areas such as rice paddies and forests, and the environment is not invaded by the survey. We think that this drone radio telemetry will be the best method for t

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Tran, Ha Thi, The-Hien Pham, Yun-Seok Mun, and Ic-Pyo Hong. "Drone Detection Using Dynamic-DBSCAN and Deep Learning in an Indoor Environment." Journal of Electromagnetic Engineering and Science 24, no. 5 (2024): 510–23. http://dx.doi.org/10.26866/jees.2024.5.r.253.

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Drones have found extensive utility in both public and personal places. Consequently, the accurate detection and tracking of drones have emerged as pivotal endeavors in terms of ensuring their optimal performance. This research paper introduces a novel application for discerning the movements of humans and drones from cloud points through the utilization of frequency-modulated continuous wave radar. The dynamic density-based spatial clustering of applications with noise (Dynamic-DBSCAN) algorithm was employed to classify cloud points into separate groups corresponding to the number of objects

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Lindenheim-Locher, Wojciech, Adam Świtoński, Tomasz Krzeszowski, et al. "YOLOv5 Drone Detection Using Multimodal Data Registered by the Vicon System." Sensors 23, no. 14 (2023): 6396. http://dx.doi.org/10.3390/s23146396.

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This work is focused on the preliminary stage of the 3D drone tracking challenge, namely the precise detection of drones on images obtained from a synchronized multi-camera system. The YOLOv5 deep network with different input resolutions is trained and tested on the basis of real, multimodal data containing synchronized video sequences and precise motion capture data as a ground truth reference. The bounding boxes are determined based on the 3D position and orientation of an asymmetric cross attached to the top of the tracked object with known translation to the object’s center. The arms of th

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Oyelami, Adekunle T., Adedayo S. Akinade, and Kingsley C. Obianefo. "Development of a real-time framework for farm monitoring using drone technology." IAES International Journal of Robotics and Automation (IJRA) 9, no. 4 (2020): 244. http://dx.doi.org/10.11591/ijra.v9i4.pp244-250.

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This work developed a cost-effective framework for agriculturists to regularly monitor their crops against intruding rodents and other security concerns using modern drone technology through configuration and deployment of an autonomous UAV which also functions as a remotely piloted vehicle. This was done by configuring a quadcopter capable of causing a disturbance when a rodent is observed through an inbuilt alarm system whose sound is amplified to be loud enough to cause the animals to leave the farm area. A framework for real-time image and live video transmission from the farm to a designa

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Adekunle, T. Oyelami, S. Akinade Adedayo, and C. Obianefo Kingsley. "Development of a real-time framework for farm monitoring using drone technology." IAES International Journal of Robotics and Automation (IJRA) 9, no. 4 (2020): 244–50. https://doi.org/10.11591/ijra.v9i4.pp244-250.

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This work developed a cost-effective framework for agriculturists to regularly monitor their crops against intruding rodents and other security concerns using modern drone technology through configuration and deployment of an autonomous UAV which also functions as a remotely piloted vehicle. This was done by configuring a quadcopter capable of causing a disturbance when a rodent is observed through an inbuilt alarm system whose sound is amplified to be loud enough to cause the animals to leave the farm area. A framework for real-time image and live video transmission from the farm to a designa

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Yao, Bodan, Weijiao Wang, Zhaojie Wang, and Qi Song. "UVPose: A Real-Time Key-Point-Based Skeleton Detection Network for a Drone Countermeasure System." Drones 9, no. 3 (2025): 214. https://doi.org/10.3390/drones9030214.

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In drone countermeasure systems, drone tracking is commonly conducted using object detection methods, which are typically limited to identifying the presence of a drone. To enhance the performance of such systems and improve the accuracy of drone flight posture prediction—while precisely capturing critical components such as rotors, mainboards, and flight trajectories—this paper introduces a novel drone key point detection model, UVPose, built upon the MMpose framework. First, we design an innovative backbone network, MDA-Net, based on the CSPNet architecture. This network improves multi-scale

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