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Classification of Low-Resolution Flying Objects in Videos Using the Machine Learning ApproachSTANCIC, I.   , VEIC, L., MUSIC, J. , GRUJIC, T. |
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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. |
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[1] Effects of Sampling Length and Overlap Ratio on EEG Mental Arithmetic Task Performance: A Comparative Study, Oran, Samet, Yıldırım, Esen, Gazi University Journal of Science, ISSN 2147-1762, 2024.
Digital Object Identifier: 10.35378/gujs.1413191 [CrossRef]
[2] Classification of High-Altitude Flying Objects Based on Radiation Characteristics with Attention-Convolutional Neural Network and Gated Recurrent Unit Network, Dai, Deen, Cao, Lihua, Liu, Yangfan, Wang, Yao, Wu, Zhaolong, Remote Sensing, ISSN 2072-4292, Issue 20, Volume 15, 2023.
Digital Object Identifier: 10.3390/rs15204985 [CrossRef]
[3] UAV Detection and Identification Using a Convolutional Neural Network, Stancic, Ivo, Juric, Toni, 2024 9th International Conference on Smart and Sustainable Technologies (SpliTech), ISBN 978-953-290-135-1, 2024.
Digital Object Identifier: 10.23919/SpliTech61897.2024.10612313 [CrossRef]
[4] Multiple Flying Object Detection using AlexNet Architecture for Aerial Surveillance Applications, Bajpai, Abhishek, Srivastava, Vaibhav, Yadav, Shruti, Sharma, Yash, 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), ISBN 979-8-3503-3509-5, 2023.
Digital Object Identifier: 10.1109/ICCCNT56998.2023.10307613 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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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.
