A Review of Deep Learning-Based Lane Detection Methods

Ting Liu; Megat Norulazmi Megat Mohamed Noor; Mohammad Faizuddin Bin Md Noor

doi:10.63944/6z3.aia

A Review of Deep Learning-Based Lane Detection Methods

Ting Liu (通讯作者)

Department of Computer Engineering Technology, Universiti Kuala Lumpur – Malaysian Institute of Information Technology

Megat Norulazmi Megat Mohamed Noor (作者)

Department of Computer Engineering Technology, Malaysian Institute of Information Technology

Mohammad Faizuddin Bin Md Noor (作者)

Department of CACS - Center for Alumni & Career Services, Universiti Kuala Lumpur – Malaysian Institute of Information Technology

DOI：https://doi.org/10.63944/6z3.aia

关键词：

lane line detection, Instance segmentation, deep learning

已出版

2026-03-31

摘要: Lane detection serves as a critical environmental perception application designed to identify lane markings utilizing onboard cameras or LiDAR systems. In recent years, concurrent with the advancement and deployment of computer vision applications, lane detection tasks have garnered substantial attention, leading to the emergence of a diverse array of lane detection methodologies. This article presents a comprehensive overview of lane detection techniques predicated on the acquisition of two-dimensional images via onboard cameras. Initially, it outlines the fundamental task of lane detection. Subsequently, it introduces datasets pertinent to lane detection. Following this, it delineates both traditional lane detection approaches and those grounded in deep learning. Ultimately, it provides an in-depth discussion of deep learning-based methods. Lane detection methodologies leveraging deep learning can be systematically categorized into four primary types: segmentation-based approaches, detection-based approaches, parameter curve-based approaches, and key point-based approaches.

参考文献

[1] Cieślik, K., Krogul, P., Muszyński, T., Przybysz, M., Rubiec, A., & Typiak, R. K. (2024). Influence of Camera Placement on UGV Teleoperation Efficiency in Complex Terrain. Applied Sciences, 14(18), 8297. DOI: https://doi.org/10.3390/app14188297
[2] Pan, Xingang, Jianping Shi, Ping Luo, Xiaogang Wang, and Xiaoou Tang. “Spatial as Deep: Spatial CNN for Traffic Scene Understanding.” Proceedings of the AAAI Conference on Artificial Intelligence 32, no. 1 (April 27, 2018). https://ojs.aaai.org/index.php/AAAI/article/view/12301. DOI: https://doi.org/10.1609/aaai.v32i1.12301
[3] Zheng, Tu, Hao Fang, Yi Zhang, Wenjian Tang, Zheng Yang, Haifeng Liu, and Deng Cai. “RESA: Recurrent Feature-Shift Aggregator for Lane Detection.” ArXiv: 2008.13719 [Cs], March 25, 2021. http://arxiv.org/abs/2008.13719.
[4] Neven, Davy, Bert De Brabandere, Stamatios Georgoulis, Marc Proesmans, and Luc Van Gool. “Towards End-to-End Lane Detection: An Instance Segmentation Approach.” In 2018 IEEE Intelligent Vehicles Symposium (IV), 286–91, 2018. https://doi.org/10.1109/IVS.2018.8500547. DOI: https://doi.org/10.1109/IVS.2018.8500547
[5] Li, Xiang, Jun Li, Xiaolin Hu, and Jian Yang. “Line-CNN: End-to-End Traffic Line Detection With Line Proposal Unit.” IEEE Transactions on Intelligent Transportation Systems 21, no. 1 (January 2020): 248–58. https://doi.org/10.1109/TITS.2019.2890870. DOI: https://doi.org/10.1109/TITS.2019.2890870
[6] Tabelini, Lucas, Rodrigo Berriel, Thiago M. Paixão, Claudine Badue, Alberto F. De Souza, and Thiago Oliveira-Santos. “Keep Your Eyes on the Lane: Real-Time Attention-Guided Lane Detection.” ArXiv:2010.12035[Cs], November 17, 2020. http://arxiv.org/abs/2010.12035.
[7] Liu, Lizhe, Xiaohao Chen, Siyu Zhu, and Ping Tan. “CondLaneNet: A Top-to-down Lane Detection Framework Based on Conditional Convolution.” ArXiv:2105.05003 [Cs], June 10, 2021. http://arxiv.org/abs/2105.05003.
[8] Qin, Zequn, Huanyu Wang, and Xi Li. “Ultra Fast Structure-Aware Deep Lane Detection.” ArXiv:2004.11757 [Cs], August 4, 2020. http://arxiv.org/abs/2004.11757.
[9] Qu, Zhan, Huan Jin, Yang Zhou, Zhen Yang, and Wei Zhang. “Focus on Local: Detecting Lane Marker from Bottom Up via Key Point.” arXiv, May 28, 2021. https://doi.org/10.48550/arXiv.2105.13680. DOI: https://doi.org/10.1109/CVPR46437.2021.01390
[10] Wang, Jinsheng, Yinchao Ma, Shaofei Huang, Tianrui Hui, Fei Wang, Chen Qian, and Tianzhu Zhang. “A Keypoint-Based Global Association Network for Lane Detection.” ArXiv:2204.07335 [Cs], April 15, 2022. http://arxiv.org/abs/2204.07335.
[11] Tabelini, Lucas, Rodrigo Berriel, Thiago M. Paixão, Claudine Badue, Alberto F. De Souza, and Thiago Oliveira-Santos. “PolyLaneNet: Lane Estimation via Deep Polynomial Regression.” ArXiv:2004.10924[Cs], July 14, 2020. http://arxiv.org/abs/2004.10924.
[12] Feng, Zhengyang, Shaohua Guo, Xin Tan, Ke Xu, Min Wang, and Lizhuang Ma. “Rethinking Efficient Lane Detection via Curve Modeling.” ArXiv:2203.02431 [Cs], March 4, 2022. http://arxiv.org/abs/2203.02431.

A Review of Deep Learning-Based Lane Detection Methods

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