과제정보
본 연구는 국토교통부/국토교통과학기술진흥원 교통물류연구사업의 연구비지원 (22TLRP-C152478-04)과 과학기술정보통신부 및 정보통신기획평가원의 대학ICT연구센터육성지원사업의 연구결과로 수행된 결과물입니다. (IITP-2022-2018-0-01426)
참고문헌
- H. J. Jang, et al. "A Study on the Construction of Deep Learning Dataset based on Virtual Lidar Sensor Point Cloud", Kookmin University, 2019.
- S. J. Yoon et al. "Development of Autonomous Vehicle Learning Data Generation System", The Journal of The Korea Institute of Intelligent Transportation Systems, Vol.19, No.5 pp 162~177.
- A. Krizhevsky, I. Sutskever and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks", Communication of ACM, Vol.60, Issue 6, pp.84-90, 2017. https://doi.org/10.1145/3065386
- K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-scale Image Recognition", International Conference on Learning Representations, 2015.
- K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recognition", The IEEE Conference on Computer Vision and Pattern Recognition, pp.770-778, 2016.
- J. I. Lee, G. S. Kwak, K. S. Kim, W. Y. Kang, D. Y. Shin, and S. H. Hwang, "Development of Virtual Simulator and Database for Deep Learning-based Object Detection", The Journal of Drive and Control, Vol.18, No.4, pp.9-18, 2021 https://doi.org/10.7839/KSFC.2021.18.4.009
- C. Yu, C. Gao, J. Wang, G. Yu, C. Shen, and N. Sang, "BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation," arXiv:2004.02147, 2020.
- L. Reiher, B. Lampe, and L. Eckstein, "A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird's Eye View," arXiv:2005.04078, 2020.
- M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, "The Cityscapes Dataset for Semantic Urban Scene Understanding," Proc. Conf. Computer Vision and Pattern Recognition, pp.3213~3223, 2016.
- J. H. Kim, "Excavator Real Time Simulation and Application," The Journal of Drive and Control, Vol.17, No.3, pp.69-75, 2020.