Simülasyon Ortamlarda Derin Öğrenme ile Nesne Tespiti
Abstract
Keywords
References
- Ashkir I, Roullier B, McQuade F, and Anjum A. (2021). 3D object recognition for virtual reality based digital twins. IEEE/ACM 8th International Conference on Big Data Computing, Applications and Technologies. United Kingdom, pp.9–17.
- Bochkovskiy A, Wang C, & Liao H.M. (2020). YOLOv4: Optimal speed and accuracy of object detection. Computer Science ArXiv abs/2004.10934.
- Fränti P, and Sieranoja S. (2018). K-means properties on six clustering benchmark datasets. Applied Intelligence 48: 4743–4759.
- Hazbon O, et al. (2019). Digital twin concept for aircraft system failure detection and correction. AIAA Aviation 2019 Forum, Texas, pp.2019-2887.
- Hendrik M, Ann-Kathrin Koschlik, and Raddatz F. (2022). Digital twin concept for aircraft components. 33rd Congress of The International Council of The Aeronautical Sciences, ICAS 2022. Sweden, ISSN 2958-4647.
- Meng W. et al. (2023). DTUAV: a novel cloud–based digital twin system for unmanned aerial vehicles. Simulation 99(1): 69-87.
- Lee Eung-Joo et al. (2021). Validation of object detection in UAV-based images using synthetic data. Proc. SPIE 11746: 584-601
- Lei L, Shen G, Zhang L, and Li Z. (2021). Toward intelligent cooperation of uav swarms: when machine learning meets digital twin. IEEE Network 35(1): 386-392.
Details
Primary Language
Turkish
Subjects
Image Processing, Modelling and Simulation
Journal Section
Research Article
Publication Date
December 20, 2023
Submission Date
June 12, 2023
Acceptance Date
September 12, 2023
Published in Issue
Year 2023 Volume: Vol:8 Number: Issue:2
Cited By
Askeri Tatbikatlarda Derin Öğrenme Yöntemleri ile Nesne Takibi
Savunma Bilimleri Dergisi
https://doi.org/10.17134/khosbd.1751897
is applied to all research papers published by JCS and 