Acknowledgement
This work was supported by Institute for Information & Communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) in 2022(No. 2017-0-00217, Development of Immersive Signage Based on Variable Transparency and Multiple Layers).
References
- OpenImage [Internet] https://storage.googleapis.com/openimages/web/ factsfigures.html
- MS CoCO [Internet] https://cocodataset.org/#home
- CIFAT-10 [Internet] http://www.cs.toronto.edu/~kriz/cifar.html
- Jin-Ho Kim, and Duck-soo Noh "Vehicle License Plate Recognition System By Edge-based Segment Image Generation", JOURNAL OF THE KOREA CONTENTS ASSOCIATION 12(3), 2012.3, 9-16 doi: https://doi.org/10.5392/JKCA.2012.12.03.009
- Hyoung-chul oh and Jong-Ho choi "A Recognition Algorithm of Car License Plate using Edge Projection and Directivity Vector", The Journal of Korean Institute of Information Technology 7(1), 2009.2, 83-92. https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE01149744
- Seung-ju Lee and Goo-man Park, "Proposal for License Plate Recognition Using Synthetic Data and Vehicle Type Recognition System", JOURNAL OF BROADCAST ENGINEERING 25(5), 2020.9, 776-788. doi: https://doi.org/10.5909/JBE.2020.25.5.776
- Jung-Hwan Kim, Joon-Hong Lim, "License Plate Detection and Recognition Algorithm using Deep Learning", journal of IKEEE 23(2), 2019.6, 642-651. doi: http://dx.doi.org/10.7471/ikeee.2019.23.2.642
- T. Bjorklund, A. Fiandrotti, M. Annarumma, G. Francini and E. Magli, "Automatic License Plate Recognition with Convolutional Neural Networks Trained on Synthetic Data," International Workshop on Multimedia Signal Processing (MMSP), pp. 1-6, 2017. doi: http://dx.doi.org/10.1109/MMSP.2017.8122260
- J. Ha "Detection of Korea License Plate by Mask R-CNN using Composite Image" Journal of Institute of Control, Robotics and Systems 26(9), 2020.9, 778-783. doi: http://dx.doi.org/10.5302/J.ICROS.2020.20.0070
- S. Kim, Y. Lee and G. Park, "Artificial license plate generation system for vehicle license plate detection" Korean society internet Information, VOL 21 NO.01 PP.33~34. 2020. https://www.eiric.or.kr/literature/ser_view.php? SnxGubun=IN KO&mode=total&searchCate=literature&gu=INME020E0&cmd=qryview&SnxIndxNum=232898&rownum=&totalCnt=2&rownum=2&q1_t=7J247KGwIOuyiO2YuO2MkA==&listUrl=L3NlYXJjaC9yZXN1bHQucGhwP1NueEd1YnVuPUlOS08mbW9kZT10b3RhbCZzZWFyY2hDYXRlPWxpdGVyYXR1cmUmcTE9JUMwJUNFJUMxJUI2KyVCOSVGOCVDOCVBMyVDNiVDNyZ4PTAmeT0w&q1=%C0%CE%C1%B6+%B9%F8%C8%A3%C6%C7&kci=
- Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. arXiv:1311.2524 (2014). doi: https://doi.org/10.48550/arXiv.1311.2524
- J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. You only look once: Unified, real-time object detection. arXiv preprint arXiv:1506.02640, 2015. doi: https://doi.org/10.48550/arXiv.1506.02640
- Joseph Redmon and Ali Farhadi. YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018. doi: https://doi.org/10.48550/arXiv.1804.02767
- W. Liu, D. Anguelov, D. Erhan, C. Szegedy, and S. Reed. Ssd: Single shot multibox detector. arXiv preprint arXiv:1512.02325, 2015. doi: https://doi.org/10.1007/978-3-319-46448-0_2
- Qijie Zhao, Tao Sheng, Yongtao Wang, Zhi Tang, Ying Chen, Ling Cai, and Haibin Ling. M2det: A single-shot object detector based on multi-level feature pyramid network. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), volume 33, pages 9259-9266, 2019. doi: https://doi.org/10.1609/aaai.v33i01.33019259
- Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Single-shot refinement neural network for object detection. arXiv preprint arXiv:1711.06897 doi: https://doi.org/10.48550/arXiv.1711.06897
- J. Cao, H. Cholakkal, R. M. Anwer, F. S. Khan, Y. Pang and L. Shao, "D2Det: Towards High Quality Object Detection and Instance Segmentation," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11482-11491, 2020. doi: https://doi.org/10.1109/CVPR42600.2020.01150
- How to Create a synthetic Dataset for computer vision [Internet] https://blog.roboflow.com/how-to-create-a-synthetic-dataset-forcomputer-vision/
- S.R Richter, V. Vineet, S. Roth, and V. Koltun "Playing for Data: Ground Truth from Computer Games." European Conference on Computer Vision -ECCV 2016 pp 102-118, 2016. doi: https://doi.org/10.48550/arXiv.1608.02192
- CamVid Database [Internet] http://mi.eng.cam.ac.uk/research/projects/VideoRec/
- SYNTHIA dataset [Internet] http://synthia-dataset.net/
- M. Muller, V. Casser, N. Smith, D.L Michels and B. Ghanem "Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation" European Conference on Computer Vision-ECCV 2018 pp 11-29. doi: 2018. https://doi.org/10.48550/arXiv.1708.05884
- NAVER LABS KITTI dataset [Internet] https://europe.naverlabs.com/Research/Computer-Vision/Proxy-Virtual-Worlds/
- J. Tremblay, et. al. "Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization". Conference on Computer Vision and Pattern Recognition Workshops(CVPRW) 2018. doi: https://doi.org/10.48550/arXiv.1804.06516
- Training Deep Networks with Synthetic Data Bridging the Reality Gap by Domain Randomization Review [Internet] https://hoya012.github.io/blog/Tutorials-of-Object-Detection-Using-Deep-Learning-performance-one/