Integrated neural network framework for multi-object detection and recognition using UAV imagery
The outcomes show that the chosen deep learning system is powerful enough to deal with the challenges of aerial vehicle analysis and gives reliable and precise results in all the aforementioned tasks. Combining several advanced models ensures that the system works smoothly even when dealing with problems like people being covered up and varying sizes.
Authors
- Nouf Abdullah Almujally (ORCID: https://orcid.org/0000-0003-4405-9912)
- Hui Liu (ORCID: https://orcid.org/0000-0002-6850-9570)
- Tingting Xue (ORCID: https://orcid.org/0000-0002-5815-7217)
- Yahya Alqahtani (ORCID: https://orcid.org/0000-0001-8622-0722)
- Ghulam Mujtaba (ORCID: https://orcid.org/0000-0002-1563-1142)
- Ahmad Jalal (ORCID: https://orcid.org/0009-0000-8421-8477)
- Mohammed Alshehri (ORCID: https://orcid.org/0009-0005-5795-3309)
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- University of Bremen (DE)
- Korea University (KR)
- Nanjing University of Information Science and Technology (CN)
- Nanjing University of Science and Technology (CN)
- Guodian Institute of Science and Technology (CN)
- King Khalid University (SA)
- Air University (PK)
Publication Details
- Journal
- Frontiers in Neurorobotics
- Published
- 2025-07-30
- DOI
- https://doi.org/10.3389/fnbot.2025.1643011
- Citations
- 11
- Primary Topic
- Infrared Target Detection Methodologies
- Type
- article
- Field-Weighted Citation Impact
- 54.93
Funders
- Princess Nourah Bint Abdulrahman University
- King Khalid University