| 2/2022 - 6 |
Classification of Low-Resolution Flying Objects in Videos Using the Machine Learning ApproachSTANCIC, I.   , VEIC, L., MUSIC, J. , GRUJIC, T. |
Extra paper information in    |
| Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science |
Download PDF  (1,493 KB) | Citation | Downloads: 830 | Views: 1,862 |
Author keywords
artificial neural networks, computer vision, feature extraction, machine learning, object detection
References keywords
detection(23), drone(14), sensors(10), machine(9), learning(9), tracking(5), system(5), sensor(5), classification(5), review(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2022-05-31
Volume 22, Issue 2, Year 2022, On page(s): 45 - 52
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.02006
Web of Science Accession Number: 000810486800006
SCOPUS ID: 85131761618
Abstract
A challenge of detecting and identifying drones has emerged due to the significant increase in recreational and commercial drones operating range, payload size, and overall capabilities. Consequently, drones may pose a risk to airspace safety or violate non-flying zone in the vicinity of vulnerable buildings. This paper presents an initial study for a machine-learning classification system applied to flying objects visible with a low resolution that is too distant from the camera to be efficiently classified by other methods. The original dataset in form of labeled high-resolution videos containing low-resolution drone, bird, and airplane objects was collected and carefully prepared. Computationally inexpensive features based on object shape and trajectory descriptors were recommended and tested with several ML models. The accuracy of the best-proposed model tested on our dataset was 98%. The results of this study demonstrate that Machine Learning classification seems to be promising and can be implemented in future multi-stage drone detection and identification system. |
| References | | | Cited By «-- Click to see who has cited this paper |
| [1] R. Merkert and J. A. Bushell, "Managing the drone revolution: A systematic literature review into the current use of airborne drones and future strategic directions for their effective control," Journal of Air Transport Management, vol. 89, 101929, 2020. [CrossRef] [Web of Science Times Cited 107] [SCOPUS Times Cited 144] [2] V. Chamola, P. Kotesh, A. Agarwal, Naren, N. Gupta, and M. Guizani, "A comprehensive review of unmanned aerial vehicle attacks and neutralization techniques," Ad Hoc Networks, vol. 111, Feb. 1 2021. [CrossRef] [Web of Science Times Cited 157] [SCOPUS Times Cited 213] [3] G. Lykou, Georgia, D. Moustakas and D. Gritzalis, "Defending airports from UAS: A Survey on cyber-attacks and counter-drone sensing technologies," Sensors, no. 12: 3537. 2020. [CrossRef] [Web of Science Times Cited 104] [SCOPUS Times Cited 149] [4] J. O'Malley, "The no drone zone," Engineering & Technology, vol. 14, no. 2, pp. 34-38, Mar 2019. [CrossRef] [SCOPUS Times Cited 23] [5] M. Huttunen, "Civil unmanned aircraft systems and security: The European approach," J. Transp. Secur, vol 12, pp 83-101, 2019. [CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 29] [6] B. Taha and A. Shoufan, "Machine learning-based drone detection and classification: State-of-the-Art in research," IEEE Access, vol. 7, pp. 138669-138682, 2019. [CrossRef] [Web of Science Times Cited 167] [SCOPUS Times Cited 253] [7] M. Azari, H. Sallouha, A. Chiumento, S. Rajendran, E. Vinogradov, and S. Pollin, "Key technologies and system trade-offs for detection and localization of amateur drones," IEEE Communications Magazine, vol. 56, no. 1, pp. 51-57, Jan. 2018. [CrossRef] [Web of Science Times Cited 88] [SCOPUS Times Cited 104] [8] I. Guvenc, F. Koohifar, S. Singh, M. Sichitiu, and D. Matolak, "Detection, tracking, and interdiction for amateur drones," IEEE Communications Magazine, vol. 56, no. 4, pp. 75-81, Apr. 2018. [CrossRef] [Web of Science Times Cited 171] [SCOPUS Times Cited 233] [9] A. Bernardini, F. Mangiatordi, E. Pallotti, and L. Capodiferro, "Drone detection by acoustic signature identification," Electronic Imaging, vol. 2017, pp. 60-64, 2017. [CrossRef] [SCOPUS Times Cited 135] [10] J. Kim, C. Park, J. Ahn, Y. Ko, J. Park, and J. Gallagher, "Real-time UAV sound detection and analysis system," 2017 IEEE Sensors Applications Symposium (Sas), 2017. [CrossRef] [SCOPUS Times Cited 103] [11] J. Busset et al., "Detection and tracking of drones using advanced acoustic cameras," Unmanned/unattended Sensors and Sensor Networks XI; and Advanced Free-Space Optical Communication Techniques and Applications, vol. 9647, 2015, Art no. 96470F. [CrossRef] [Web of Science Times Cited 72] [SCOPUS Times Cited 124] [12] S. Grac, P. Beno, F. Duchon, M. Dekan, and M. Tolgyessy, "Automated detection of multi-rotor uavs using a machine-learning approach," Applied System Innovation, vol. 3, no. 3, Sep. 2020, Art no. 29. [CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 9] [13] U. Seidaliyeva, D. Akhmetov, L. Ilipbayeva, and E. Matson, "Real-time and accurate drone detection in a video with a static background," Sensors, Article vol. 20, no. 14, Jul. 2020, Art no. 3856. [CrossRef] [Web of Science Times Cited 88] [SCOPUS Times Cited 135] [14] A. Rozantsev, V. Lepetit, and P. Fua, "Detecting flying objects using a single moving camera," IEEE Transactions on Pattern Analysis and Machine Intelligence, Article vol. 39, no. 5, pp. 879-892, May 2017. [CrossRef] [Web of Science Times Cited 160] [SCOPUS Times Cited 208] [15] D. Lee, W. La, and H. Kim, "Drone detection and identification system using artificial intelligence," International Conference on Information and Communication Technology Convergence (ICTC), 2018. pp. 1131-1133. [CrossRef] [SCOPUS Times Cited 89] [16] E. Unlu, E. Zenou, and N. Riviere, "Using shape descriptors for UAV detection," Electronic Imaging, vol. 2018, pp. 128-1-128-5, 2018 [17] D. Lee, "CNN-based single object detection and tracking in videos and its application to drone detection," Multimedia Tools and Applications, vol. 80, no. 26-27, pp. 34237-34248, Nov. 2021. [CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 37] [18] S. Singha and B. Aydin, "Automated drone detection using YOLOv4," Drones, vol. 5, no. 3, Sep 2021, Art no. 95, [CrossRef] [Web of Science Times Cited 37] [SCOPUS Times Cited 67] [19] T. Kashiyama, H. Sobue, and Y. Sekimoto, "Sky monitoring system for flying object detection using 4K resolution camera," Sensors, vol. 20, no. 24, Dec. 2020, Art no. 7071. [CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 9] [20] M. Jahangir and C. Baker, "Robust detection of Micro-UAS drones with L-band 3-D holographic radar," Sensor Signal Processing for Defence (SSPD), 2016, pp. 1-5. [CrossRef] [SCOPUS Times Cited 39] [21] W. Zhang and G. Li, "Detection of multiple micro-drones via cadence velocity diagram analysis," Electronics Letters, vol. 54, no. 7, pp. 441-442, Apr. 5 2018. [CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 47] [22] A. Coluccia, G. Parisi, and A. Fascista, "Detection and classification of multirotor drones in radar sensor networks: A review," Sensors, vol. 20, no. 15, Aug. 2020, Art no. 4172. [CrossRef] [Web of Science Times Cited 73] [SCOPUS Times Cited 89] [23] P. Klaer et al., "An investigation of rotary drone HERM line spectrum under manoeuvering conditions," Sensors, vol. 20, no. 20, Oct. 2020, Art no. 5940. [CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 31] [24] S. Basak, S. Rajendran, S. Pollin, and B. Scheers, "Combined RF-based drone detection and classification," IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 1, pp. 111-120, Mar 2022. [CrossRef] [Web of Science Times Cited 38] [SCOPUS Times Cited 57] [25] M. Ezuma, F. Erden, C. Anjinappa, O. Ozdemir, and I. Guvenc, "Micro-UAV detection and classification from RF fingerprints using machine learning techniques," 2019 IEEE Aerospace Conference, 2019, pp. 1-13. [CrossRef] [SCOPUS Times Cited 137] [26] S. Al-Emadi and F. Al-Senaid, "Drone detection approach based on radio-frequency using convolutional neural network," 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), 2020. pp. 29-34, [CrossRef] [SCOPUS Times Cited 90] [27] F. Svanstrom, "Drone detection and classification using machine learning and sensor fusion," Master's Programme in Embedded and Intelligent Systems, School of Information Technology, Halmstad University, Halmstad, Sweeden, urn:nbn:se:hh:diva-42141, 2020 [28] S. Jovanoska, M. Brotje and W. Koch, "Multisensor data fusion for UAV detection and tracking," 19th International Radar Symposium (IRS), 2018, pp. 1-10, 2018. [CrossRef] [SCOPUS Times Cited 25] [29] S. Samaras et al., "Deep learning on multi sensor data for counter UAV applications - A systematic review," Sensors, vol. 19, no. 22, Nov. 2019, Art no. 4837. [CrossRef] [Web of Science Times Cited 111] [SCOPUS Times Cited 148] [30] F. Svanstrom, C. Englund, and F. Alonso-Fernandez, "Real-time drone detection and tracking with visible, thermal and acoustic sensors," 25th International Conference on Pattern Recognition (ICPR), pp. 7265-7272, 2021. [CrossRef] [Web of Science Times Cited 57] [SCOPUS Times Cited 73] [31] J. Wojtanowski, M. Zygmunt, T. Drozd, M. Jakubaszek, M. Zyczkowski, and M. Muzal, "Distinguishing drones from birds in a UAV searching laser scanner based on echo depolarization measurement," Sensors, vol. 21, no. 16, Aug. 2021, Art no. 5597. [CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 12] [32] A. Burkov, The hundred-page machine learning book. Andriy Burkov, 2019 [33] J. Brownlee, Master machine learning algorithms: Discover how they work and implement them from scratch. Machine Learning Mastery, 2016 [34] T. A. Sjaardema, C. S. Smith, and G. C. Birch, "History and evolution of the Johnson criteria," Sandia National Lab., Albuquerque, United States, 2015. [CrossRef] [35] T. Goh, S. Basah, H. Yazid, M. Safar, and F. Saad, "Performance analysis of image thresholding: Otsu technique," Measurement, vol. 114, pp. 298-307, Jan 2018. [CrossRef] [Web of Science Times Cited 177] [SCOPUS Times Cited 239] [36] D. Sun, S. Roth, and M. J. Black, "Secrets of optical flow estimation and their principles," 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2432-2439, 2010. [CrossRef] [Web of Science Times Cited 963] [SCOPUS Times Cited 1313] [37] G. Ciaburro, MATLAB for machine learning. Packt Publishing, 2017. [38] G. Bradski, "The OpenCV library," Dr. Dobb's Journal of Software Tools, vol. 120, pp. 122-125, 2000 Web of Science® Citations for all references: 2,689 TCR SCOPUS® Citations for all references: 4,364 TCR Web of Science® Average Citations per reference: 69 ACR SCOPUS® Average Citations per reference: 112 ACR TCR = Total Citations for References / ACR = Average Citations per Reference We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more Citations for references updated on 2024-11-14 03:14 in 216 seconds. Note1: Web of Science® is a registered trademark of Clarivate Analytics. Note2: SCOPUS® is a registered trademark of Elsevier B.V. Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site. |
Copyright ©2001-2024
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
