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The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

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References

1 
Liangkai Liu et al., “Computing Systems for Autonomous Driving: State-of-the-Art and Challenges,” IEEE Internet Things, vol. 8, no. 8, pp. 6469–6486, 2021. DOI: 10.48550/arXiv.2009.14349DOI
2 
Xuan Wang et al., “Multi-Sensor Fusion Technology for 3D Object Detection in Autonomous Driving: A Review,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 2, pp. 1148-1165, 2024. DOI: 10.1109/TITS.2023.3317372DOI
3 
E. DANDIL, and K. K. ÇEVİK, “Computer Vision Based Distance Measurement System using Stereo Camera View,” 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1-4, 2019. DOI: 10.1109/ISMSIT.2019.8932817DOI
4 
J. Redmon, and A. Farhadi, “YOLO9000: Better, Faster, Stronger,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517-6525, 2017. DOI: 10.1109/CVPR.2017.690DOI
5 
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? The KITTI vision benchmark suite,” 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3354-3361, 2012. DOI: 10.1109/CVPR.2012.6248074DOI
6 
H. Kim, and S. Park, “Monocular Camera based Real-Time Object Detection and Distance Estimation Using Deep Learning,” The Journal of Korea Robotics Society, vol. 14, no. 4, pp. 357–362, 2019. DOI: 10.7746/jkros.2019.14.4.357DOI
7 
A. Masoumian et al., “Absolute distance prediction based on deep learning object detection and monocular depth estimation models,” Artificial Intelligence Research and Development, pp. 325-334, 2021. DOI: 10.3233/FAIA210151DOI
8 
N. Carion et al., “End-to-End object detection with Transformers,” Computer Vision – ECCV 2020, vol. 12346, pp. 213–229, 2020. DOI: 10.1007/978-3-030-58452-8_13DOI
9 
Tianqi Chen, and Carlos Guestrin, “Xgboost: A scalable tree boosting system,” arXiv, 2016. DOI: 10.48550/arXiv.1603.02754DOI
10 
Seungyoo Lee et al., “Vehicle Distance Estimation from a Monocular Camera for Advanced Driver Assistance Systems,” Symmetry, vol. 14, no. 12, 2657, 2022. DOI: 10.3390/sym14122657DOI
11 
Yang, Lihe et al., “Depth Anything V2,” arXiv, 2024. DOI: 10.48550/arXiv.2406.09414DOI
12 
Yang, Lihe, and Kang et al., “Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data,” 2024 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10371-10381, 2024. DOI: 10.48550/arXiv.2401.10891DOI
13 
S. Song et al., “SUN RGB-D: A RGB-D scene understanding benchmark suite,” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 567-576, 2015. DOI: 10.1109/CVPR.2015.7298655DOI
14 
T. Koch et al., “Evaluation of CNN-based Single-Image Depth Estimation Methods,” Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018. DOI: 10.48550/arXiv.1805.01328DOI
15 
Roberts, Mike, and Nathan Paczan, “Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding,” 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10912-10922, 2021. DOI: 10.48550/arXiv.2011.02523DOI
16 
Yohann Cabon, Naila Murray, and Martin Humenberger, “Virtual kitti 2,” arXiv, 2020. DOI: 10.48550/arXiv.2001.10773DOI
17 
Vasiljevic et al., “DIODE: A Dense Indoor and Outdoor DEpth Dataset,” arXiv, 2019. DOI: 10.48550/arXiv.1908.00463DOI
18 
Shariq Farooq Bhat et al., “Zoedepth: Zero-shot transfer by combining relative and metric depth,” arXiv, 2023. DOI: 10.48550/arXiv.2302.12288DOI
19 
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779-788, 2016. DOI: 10.1109/CVPR.2016.91DOI
20 
Z. Zou et al., “Object Detection in 20 Years: A Survey,” in Proceedings of the IEEE, vol. 111, no. 3, pp. 257-276, 2023. DOI: 10.1109/JPROC.2023.3238524DOI
21 
P. Jiang et al., “A Review of YOLO Algorithm Developments,” Procedia Computer Science, vol. 199, pp. 1066-1073, 2022. DOI: 10.1016/j.procs.2022.01.135DOI
22 
Lin, TY et al., “Microsoft COCO: Common Objects in Context,” Computer Vision – ECCV 2014, vol 8693, pp. 740-755, 2014. DOI: 10.1007/978-3-319-10602-1_48DOI
23 
NVIDIA, “NVIDIA Jetson Orin”, https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/URL
24 
stereolabs, “stereolabs Docs: API Reference, Tutorials, and Integration”, https://www.stereolabs.com/docsURL