지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.2
- 수록면
- 68 - 73 (6page)
- DOI
- 10.5302/J.ICROS.2024.23.0203
이용수
초록· 키워드
오류제보하기
We explored the recognition of a landing pad by using semantic image segmentation with the integration of a virtual reality dataset. To create a virtual 3D environment similar to the real environment, we applied this technology to create a 3D map based on aerial photos. In the resulting virtual reality, we created a scenario in which a drone equipped with a camera acquired images of the landing pad. The program was designed to place a landing pad in a virtual reality, acquire images of the landing pad using a camera in virtual reality, and automatically perform image segmentation labeling for the landing pad. An experiment was conducted in which a deep neural network recognized the landing pad from the images captured by an actual drone. The network used only the dataset constructed with automatic labeling technology in virtual reality. The experimental results confirmed that the actual landing pad image could be recognized even by using only the dataset collected in virtual reality.
목차
Abstract
I. 서론
II. 가상 공간을 구현하기 위한 3차원 재구성 기술
III. 심층 신경망 기반 의미론적 영상 분할 기법
IV. 실험 결과
V. 결론
REFERENCES
참고문헌 (10)
참고문헌 신청
M. S. Alam and J. Oluoch, “A survey of safe landing zone detection techniques for autonomous unmanned aerial vehicles (UAVs),” Expert Systems with Applications, vol. 179, 2021.
S. Kim, “Neural network-based battery anomaly detection method for electric-powered UAS,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 27, no. 3, pp. 202-207, 2021.
S. Kim, “Development of virtual reality data collection technology for deep neural network-based landing point recognition methods,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 29, no. 5, pp. 392-397, 2023.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional network for biomedical image segmentation,” International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234-241, 2015.
Y. Cabon, N. Murray, and M. Humenberger, “Virtual KITTI 2,” arXiv preprint arXiv:2001.10773, 2020.
S. Kim, “Neural network-based battery anomaly detection method for electric-powered UAS,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 27, no. 3, pp. 202-207, 2021.
S. Kim, “Development of virtual reality data collection technology for deep neural network-based landing point recognition methods,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 29, no. 5, pp. 392-397, 2023.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional network for biomedical image segmentation,” International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234-241, 2015.
Y. Cabon, N. Murray, and M. Humenberger, “Virtual KITTI 2,” arXiv preprint arXiv:2001.10773, 2020.
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