Object Detection and Tracking with UAV Data Using Deep Learning
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The YOLOv3 with Darknet-53 achieves an F1 score of 92.10% on a combined dataset of Custom, aerial-cars, and UAVDT.
“[94] YOLOv3 | | Darknet-53 | Custom + aerial-cars dataset [73] + UAVDT | 2720 × 1530 (30fps) | Different altitudes | Different angles | -- | -- | 92.10 | -- | --”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [94]
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- Authors
- Micheal, A. A., Vani, K., Sanjeevi, S., & Lin, C.-H.
- Affiliations
- Anna University, Chennai (IN)
- National Cheng Kung University (TW)
- Corresponding author
- Micheal, A. Ancy
- Venue
- Journal of the Indian Society of Remote Sensing
- Published
- 2020-11-02
- Volume
- 49
- Issue
- 3
- Pages
- 463-469
- ISSN
- 0255-660X (ISSN-L)
- 0974-3006
- OpenAlex
- W3096582369
- Cited by
- 58
- References
- 20
- Citations by year
- 2026: 5 · 2025: 19 · 2024: 7 · 2023: 17 · 2022: 9 · 2021: 1
- FWCI
- 2.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the Indian Society of Remote Sensing (publishedVersion)
- Funders
- ISPRS Scientific Initiatives 2019
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- object detection, object tracking, UAV imagery, deep supervision, long short-term memory, domain mismatch
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