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This paper presents a novel parametric curve-based method for lane detection in RGB images.
Bezier curve fitting
Tim A Pastva · 1998
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Technical Standard of Highway Engineering
MOT Highway Department and Highway Engineering Committee under China Association for Engineering Construction Standardization · 2004
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Recent progress in road and lane detection: a survey
Aharon Bar Hillel, Ronen Lerner, Dan Levi, and Guy Raz · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Standard Specifications for Construction of Roads and Bridges on Federal Highway Projects
Federal Highway Administration under United States Department of Transportation · 2014
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Semantic image segmentation with deep convolutional nets and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille · 2015
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Fast r-cnn
Ross Girshick · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Deeplanes: End-to-end lane position estimation using deep neural networksa
Alexandru Gurghian, Tejaswi Koduri, Smita V Bailur, Kyle J Carey, and Vidya N Murali · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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https://github.com/TuSimple/tusimple-benchmark , 2017
TuSimple benchmark · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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El-gan: Embedding loss driven generative adversarial networks for lane detection
Mohsen Ghafoorian, Cedric Nugteren, Nóra Baka, Olaf Booij, and Michael Hofmann · 2018
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Towards end-to-end lane detection: an instance segmentation approach
Davy Neven, Bert De Brabandere, Stamatios Georgoulis, Marc Proesmans, and Luc Van Gool · 2018
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Spatial as deep: Spatial cnn for traffic scene understanding
Xingang Pan, Jianping Shi, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2018
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Semantic foggy scene understanding with synthetic data
Christos Sakaridis, Dengxin Dai, and Luc Van Gool · 2018
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Unsupervised labeled lane markers using maps
Karsten Behrendt and Ryan Soussan · 2019
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Learning lightweight lane detection cnns by self attention distillation
Yuenan Hou, Zheng Ma, Chunxiao Liu, and Chen Change Loy · 2019
Abcnet: Real-time scene text spotting with adaptive bezier-curve network
Yuliang Liu, Hao Chen, Chunhua Shen, Tong He, Lianwen Jin, and Liangwei Wang · 2020
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Ultra fast structure-aware deep lane detection
Zequn Qin, Huanyu Wang, and Xi Li · 2020
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Polylanenet: Lane estimation via deep polynomial regression
Lucas Tabelini, Rodrigo Berriel, Thiago M Paixao, Claudine Badue, Alberto F De Souza, and Thiago Oliveira-Santos · 2020
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Curvelane-nas: Unifying lane-sensitive architecture search and adaptive point blending
Hang Xu, Shaoju Wang, Xinyue Cai, Wei Zhang, Xiaodan Liang, and Zhenguo Li · 2020
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End-to-end lane marker detection via row-wise classification
Seungwoo Yoo, Hee Seok Lee, Heesoo Myeong, Sungrack Yun, Hyoungwoo Park, Janghoon Cho, and Duck Hoon Kim · 2020
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Self-driving cars: A survey
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Line-cnn: End-to-end traffic line detection with line proposal unit
Xiang Li, Jun Li, Xiaolin Hu, and Jian Yang · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Fastdraw: Addressing the long tail of lane detection by adapting a sequential prediction network
Jonah Philion · 2019
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Generalized intersection over union: A metric and a loss for bounding box regression
Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian Reid, and Silvio Savarese · 2019
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Fcos: Fully convolutional one-stage object detection
Zhi Tian, Chunhua Shen, Hao Chen, and Tong He · 2019
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End-to-end lane detection through differentiable least-squares fitting
Wouter Van Gansbeke, Bert De Brabandere, Davy Neven, Marc Proesmans, and Luc Van Gool · 2019
Cited alongside, same era.
Claudine Badue, Rânik Guidolini, Raphael Vivacqua Carneiro, Pedro Azevedo, Vinicius B Cardoso, Avelino Forechi, Luan Jesus, Rodrigo Berriel, Thiago M Paixao, Filipe Mutz, et al · 2021
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You only look one-level feature
Qiang Chen, Yingming Wang, Tong Yang, Xiangyu Zhang, Jian Cheng, and Jian Sun · 2021
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Condlanenet: a top-to-down lane detection framework based on conditional convolution
Lizhe Liu, Xiaohao Chen, Siyu Zhu, and Ping Tan · 2021
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End-to-end lane shape prediction with transformers
Ruijin Liu, Zejian Yuan, Tie Liu, and Zhiliang Xiong · 2021
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Focus on local: Detecting lane marker from bottom up via key point
Zhan Qu, Huan Jin, Yang Zhou, Zhen Yang, and Wei Zhang · 2021
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Keep your eyes on the lane: Real-time attention-guided lane detection
Lucas Tabelini, Rodrigo Berriel, Thiago M Paixao, Claudine Badue, Alberto F De Souza, and Thiago Oliveira-Santos · 2021
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Night-time scene parsing with a large real dataset
Xin Tan, Ke Xu, Ying Cao, Yiheng Zhang, Lizhuang Ma, and Rynson W. H. Lau · 2021
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End-to-end object detection with fully convolutional network
Jianfeng Wang, Lin Song, Zeming Li, Hongbin Sun, Jian Sun, and Nanning Zheng · 2021
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Resa: Recurrent feature-shift aggregator for lane detection
Tu Zheng, Hao Fang, Yi Zhang, Wenjian Tang, Zheng Yang, Haifeng Liu, and Deng Cai · 2021
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