Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 30, 24 January 2024
Open
Access | Article
Unmanned aerial vehicle face recognition technology research progress
* Author to whom correspondence should be addressed.
Abstract
In recent years, face recognition technology has become a significant advance in the area of biometrics and machine vision. It paves the way for a wide range of applications, including security systems, access control, surveillance, and user authentication. These applications are undoubtedly a major innovation in the field of UAV, which will greatly expand and extend its application scenarios. This paper shows a compositive review of UAV facial identification technology, including its basic principles, techniques, challenges, and ethical considerations. According to the different stages, this paper focuses on RCNN and YOLO, two more widely used target detection technologies and their respective technical iterations, and through the comparison of the two in terms of technical characteristics and application scenarios, the advantages of the two are obtained, and combined with the current UAV workflow. Get the stage in which they play a specific role. This paper reviews the existing literature and research on face recognition, aiming to help people better understand the current process of this technology and its wider social application and impact.
Keywords
UAV, face recognition technology, RCNN, YOLO
References
1. Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv.org
2. Girshick R. Fast R-CNN[J]. Computer Science, 2015.
3. Everingham, M., Eslami, S. M., A., Van Gool, L., Williams, C. K., I., et al. (2015). The pascal visual object classes challenge: A retrospective. International Journal of Computer Vision, 111(1), 98-136
4. Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”, Advances in Neural Information Processing Systems, 2015.
5. Joseph, Redmon., Santosh, K., Divvala., Ross, Girshick., Ali, Farhadi. (2016). You Only Look Once: Unified, Real-Time Object Detection. arXiv.org
6. Chuyin, Li., Lu, Li., Hongliang, Jiang., Kaiheng, Weng., Yifei, Geng., Lin, Li., Zaidan, Ke., Qingyuan, Li., Meng, Cheng., Weiqiang, Nie., Yiduo, Li., Yufei, Liang., Linyuan, Zhou., Xiaoming, Xu., Xiangxiang, Chu., Xiaoming, Wei., Xiaolin, Wei. (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. arXiv.org
7. Chien-Yao, Wang., Alexey, Bochkovskiy., Hong-Yuan, Mark, Liao. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.org, abs/2207.02696
8. Weikai, He. (2023). YOLOv7-UAV: An Unmanned Aerial Vehicle Image Object Detection Algorithm Based on Improved YOLOv7. Electronics, doi: 10.3390/electronics12143141
9. Jordan, Kupec. (2023). Real-Time Flying Object Detection with YOLOv8. arXiv.org
10. Shifeng Zhang, Longyin Wen, Xiao Bian, Zhen Lei, and Stan Z. Li, “Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd”, Computer Science, 2018.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-283-1
- ISBN (Online)
- 978-1-83558-284-8
- Published Date
- 24 January 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/30/20241073
- Copyright
- 24 January 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited