Vision-Based Support for the Detection and Recognition of Drones with Small Radar Cross Sections
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...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/10/2235 |
| _version_ | 1797600355954982912 |
|---|---|
| author | Safa E. Abdelsamad Mohammed A. Abdelteef Othman Y. Elsheikh Yomna A. Ali Tarik Elsonni Maha Abdelhaq Raed Alsaqour Rashid A. Saeed |
| author_facet | Safa E. Abdelsamad Mohammed A. Abdelteef Othman Y. Elsheikh Yomna A. Ali Tarik Elsonni Maha Abdelhaq Raed Alsaqour Rashid A. Saeed |
| author_sort | Safa E. Abdelsamad |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T03:46:58Z |
| format | Article |
| id | doaj.art-a4d8c914b7524efb9450925e7a2e963b |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T03:46:58Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-a4d8c914b7524efb9450925e7a2e963b2023-11-18T01:09:36ZengMDPI AGElectronics2079-92922023-05-011210223510.3390/electronics12102235Vision-Based Support for the Detection and Recognition of Drones with Small Radar Cross SectionsSafa E. Abdelsamad0Mohammed A. Abdelteef1Othman Y. Elsheikh2Yomna A. Ali3Tarik Elsonni4Maha Abdelhaq5Raed Alsaqour6Rashid A. Saeed7Department of Aeronautical Engineering, Faculty of Engineering, Sudan University of Science and Technology (SUST), Khartoum 11116, SudanDepartment of Aeronautical Engineering, Faculty of Engineering, Sudan University of Science and Technology (SUST), Khartoum 11116, SudanDepartment of Aeronautical Engineering, Faculty of Engineering, Sudan University of Science and Technology (SUST), Khartoum 11116, SudanDepartment of Aeronautical Engineering, Faculty of Engineering, Sudan University of Science and Technology (SUST), Khartoum 11116, SudanDepartment of Aeronautical Engineering, Faculty of Engineering, Sudan University of Science and Technology (SUST), Khartoum 11116, SudanDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Information Technology, College of Computing and Informatics, Saudi Electronic University, P.O. Box 93499, Riyadh 11673, Saudi ArabiaDepartment of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDrones 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.https://www.mdpi.com/2079-9292/12/10/2235cross sectionsdronesvision supportdetectionunmanned aerial vehicles |
| spellingShingle | Safa E. Abdelsamad Mohammed A. Abdelteef Othman Y. Elsheikh Yomna A. Ali Tarik Elsonni Maha Abdelhaq Raed Alsaqour Rashid A. Saeed Vision-Based Support for the Detection and Recognition of Drones with Small Radar Cross Sections Electronics cross sections drones vision support detection unmanned aerial vehicles |
| title | Vision-Based Support for the Detection and Recognition of Drones with Small Radar Cross Sections |
| title_full | Vision-Based Support for the Detection and Recognition of Drones with Small Radar Cross Sections |
| title_fullStr | Vision-Based Support for the Detection and Recognition of Drones with Small Radar Cross Sections |
| title_full_unstemmed | Vision-Based Support for the Detection and Recognition of Drones with Small Radar Cross Sections |
| title_short | Vision-Based Support for the Detection and Recognition of Drones with Small Radar Cross Sections |
| title_sort | vision based support for the detection and recognition of drones with small radar cross sections |
| topic | cross sections drones vision support detection unmanned aerial vehicles |
| url | https://www.mdpi.com/2079-9292/12/10/2235 |
| work_keys_str_mv | AT safaeabdelsamad visionbasedsupportforthedetectionandrecognitionofdroneswithsmallradarcrosssections AT mohammedaabdelteef visionbasedsupportforthedetectionandrecognitionofdroneswithsmallradarcrosssections AT othmanyelsheikh visionbasedsupportforthedetectionandrecognitionofdroneswithsmallradarcrosssections AT yomnaaali visionbasedsupportforthedetectionandrecognitionofdroneswithsmallradarcrosssections AT tarikelsonni visionbasedsupportforthedetectionandrecognitionofdroneswithsmallradarcrosssections AT mahaabdelhaq visionbasedsupportforthedetectionandrecognitionofdroneswithsmallradarcrosssections AT raedalsaqour visionbasedsupportforthedetectionandrecognitionofdroneswithsmallradarcrosssections AT rashidasaeed visionbasedsupportforthedetectionandrecognitionofdroneswithsmallradarcrosssections |