Vision-Based Support for the Detection and Recognition of Drones with Small Radar Cross Sections-Reference-Cited by-同舟云学术

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

Published:2023-05-15 Issue:10 Volume:12 Page:2235
ISSN:2079-9292
Container-title:Electronics
language:en
Short-container-title:Electronics

Author:

Abdelsamad Safa E.¹,Abdelteef Mohammed A.¹,Elsheikh Othman Y.¹,Ali Yomna A.¹,Elsonni Tarik¹,Abdelhaq Maha²,Alsaqour Raed³^ORCID,Saeed Rashid A.⁴^ORCID

Affiliation:

1. Department of Aeronautical Engineering, Faculty of Engineering, Sudan University of Science and Technology (SUST), Khartoum 11116, Sudan

2. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

3. Department of Information Technology, College of Computing and Informatics, Saudi Electronic University, P.O. Box 93499, Riyadh 11673, Saudi Arabia

4. Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

Abstract

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.

Funder

Princess Nourah bint Abdulrahman University Researchers Supporting

Deanship of Scientific Research, Taif University

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Link

https://www.mdpi.com/2079-9292/12/10/2235/pdf

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