Target detection and classification via EfficientDet and CNN over unmanned aerial vehicles
The results indicate that the proposed model significantly enhances vehicle detection and classification in aerial images, surpassing existing methods and offering notable improvements for traffic monitoring systems.
Authors
- Touseef Sadiq (ORCID: https://orcid.org/0000-0001-6603-3639)
- Muhammad Hanzla (ORCID: https://orcid.org/0009-0000-5494-8743)
- Asaad Algarni (ORCID: https://orcid.org/0000-0002-0817-5775)
- Bayan Ibrahimm Alabdullah (ORCID: https://orcid.org/0000-0002-6252-1800)
- Naif Al Mudawi
- Muhammad Ovais Yusuf (ORCID: https://orcid.org/0000-0003-4700-7317)
- Hameedur Rahman (ORCID: https://orcid.org/0000-0001-8892-9911)
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- Northern Border University (SA)
- University of Agder (NO)
- Najran University (SA)
- Air University (PK)
Publication Details
- Journal
- Frontiers in Neurorobotics
- Published
- 2024-08-30
- DOI
- https://doi.org/10.3389/fnbot.2024.1448538
- Citations
- 23
- Primary Topic
- Advanced Neural Network Applications
- Type
- article
- Field-Weighted Citation Impact
- 5.73
Funders
- Princess Nourah Bint Abdulrahman University