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NTIS 바로가기Sensors, v.21 no.2, 2021년, pp.400 -
Lu, Sheng (School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China) , Luo, Zhaojie (lusheng@cqupt.edu.cn (S.L.)) , Gao, Feng (S182101022@stu.cqupt.edu.cn (Z.L.)) , Liu, Mingjie (School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China) , Chang, KyungHi (lusheng@cqupt.edu.cn (S.L.)) , Piao, Changhao (S182101022@stu.cqupt.edu.cn (Z.L.))
Lane detection is a significant technology for autonomous driving. In recent years, a number of lane detection methods have been proposed. However, the performance of fast and slim methods is not satisfactory in sophisticated scenarios and some robust methods are not fast enough. Consequently, we pr...
1. Aly M. Real time detection of lane markers in urban streets Proceedings of the 2008 IEEE Intelligent Vehicles Symposium Eindhoven, The Netherlands 4–6 June 2008 7 12
2. Zhou S. Jiang Y. Xi J. Gong J. Xiong G. Chen H. A novel lane detection based on geometrical model and Gabor filter Proceedings of the 2010 IEEE Intelligent Vehicles Symposium San Diego, CA, USA 21–24 June 2010 59 64
3. Mccall J. Trivedi M. Video-based lane estimation and tracking for driver assistance: Survey, system, and evaluation Proceedings of the IEEE Transactions on Intelligent Transportation Systems New York, NY, USA 6 March 2006 20 27
4. Loose H. Franke U. Stiller C. Kalman particle filter for lane recognition on rural roads Proceedings of the 2009 IEEE Intelligent Vehicles Symposium Xi’an, China 3–5 June 2009 60 65
5. Chiu K. Lin S. Lane detection using color-based segmentation Proceedings of the 2005 IEEE Intelligent Vehicles Symposium Las Vegas, NV, USA 6–8 June 2005 706 711
7. Teng Z. Kim J.H. Kang D.J. Real-time Lane detection by using multiple cues Proceedings of the 2010 International Conference on Control, Automation and Systems Gyeonggi-do, Korea 27–30 October 2010 2334 2337
8. Borkar A. Hayes M. Smith M. Polar randomized Hough Transform for lane detection using loose constraints of parallel lines Proceedings of the 2011 International Conference on Acoustics, Speech and Signal Processing Prague, Czech Republic 22–27 May 2011 1037 1040
9. Hur J. Kang S.N. Seo S.W. Multi-lane detection in urban driving environments using conditional random fields Proceedings of the 2013 IEEE Intelligent Vehicles Symposium Gold Coast, QLD, Australia 23–26 June 2013 1297 1302
10. Neven D. Brabandere B.D. Georgoulis S. Towards end-to-end lane detection: An instance segmentation approach Proceedings of the 2018 IEEE Intelligent Vehicles Symposium Changshu, China 26–30 June 2018 268 291
11. Paszke A. Chaurasia A. Kim S. Culurciello E. ENet: A deep neural network architecture for real-time semantic segmentation arXiv 2016 1606.02147
12. Brabandere B.D. Neven D. Gool L.V. Semantic instance segmentation for autonomous driving Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops Eindhoven, The Netherlands 21–26 July 2017 478 480
13. Ghafoorian M. Nugteren C. Baka N. Booij O. Hofmann M. EL-GAN: Embedding loss driven generative adversarial networks for lane detection Proceedings of the European Conference on Computer Vision Workshops Munich, Germany 8–14 September 2018 256 272
14. Chen L. Zhu Y. Papandreou G. Schrof F. Adam H. Encoder-decoder with atrous separable convolution for semantic image segmentation Proceedings of the European Conference on Computer Vision Workshops Munich, Germany 8–14 September 2018 801 818
15. Dosovitskiy A. Fischer P. Ilg E. Hausser P. Hazırbas C. Golkov V. FlowNet: Learning optical flow with convolutional networks Proceedings of the IEEE International Conference on Computer Vision Santiago, Chile 7–13 December 2015 2758 2766
16. Liu P. King I. Lyu M.R. Xu J. DDFlow: Learning optical flow with unlabeled data distillation Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence Honolulu, HI, USA 27 January–1 February 2019 8770 8777
17. Yu H. Zhang W. Method of vehicle distance measurement for following car based on monocular vision J. Southeast Univ. Nat. Sci. Ed. 2012 3 542 546
19. Yu C. Wang J. Peng C. Gao C. Yu G. Sang N. BiSeNet: Bilateral segmentation network for real-time semantic segmentation Proceedings of the European Conference on Computer Vision Munich, Germany 8–14 September 2018 334 349
20. Zhao H. Qi X. Shen X. Shi J. Jia J. ICNet for real-time semantic segmentation on high-resolution images Proceedings of the European Conference on Computer Vision Munich, Germany 8–14 September 2018 418 434
21. Chen L.C. Papandreou G. Kokkinos I. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs IEEE Trans. Pattern Anal. Mach. Intell. 2018 40 834 848 10.1109/TPAMI.2017.2699184 28463186
22. Zhao H. Shi J. Qi X. Wang X. Jia J. Pyramid scene parsing network Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Eindhoven, The Netherlands 27 April 2017
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