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🏆 Awesome-Lane-Detection

Awesome GitHub CV-Action

demo-image
Lane Detection Demo

Content 🎯

Paper 📑

PWC

https://paperswithcode.com/task/lane-detection

2020

2019

2018

2017

Code 💻

Lane Detection(Paper with Code)

https://github.com/cardwing/Codes-for-Lane-Detection

https://github.com/karstenBehrendt/unsupervised_llamas

https://github.com/wvangansbeke/LaneDetection_End2End

https://github.com/georgesung/advanced_lane_detection

https://github.com/MaybeShewill-CV/lanenet-lane-detection

https://github.com/XingangPan/SCNN

https://github.com/davidawad/Lane-Detection

https://github.com/yang1688899/CarND-Advanced-Lane-Lines

https://github.com/SeokjuLee/VPGNet

https://github.com/mvirgo/MLND-Capstone:Lane Detection with Deep Learning

https://github.com/galenballew/SDC-Lane-and-Vehicle-Detection-Tracking

https://github.com/shawshany/Advance_LaneFinding

Blog/Tutorial ✏️

Lane Detection with Deep Learning (Part 1)

Simple Lane Detection with OpenCV

Finding Lane Lines — Simple Pipeline For Lane Detection

Building a lane detection system using Python 3 and OpenCV

Tutorial: Build a lane detector

PolyLaneNet:最新车道线检测开源算法,多项式回归实时高效

ECCV2020 | 300+FPS!浙大提出一种超快速车道线检测方法

ECCV2020 | Gen-LaneNet:百度Apollo提出两阶段的3D车道线检测算法,已开源

CurcveLane-NAS:华为&中大提出一种结合NAS的曲线车道检测算法

韩国研究院最新开源——基于关键点估计和实例分割的车道线检测方法

鲁汶大学提出可端到端学习的车道线检测算法

【Papers】Lane-Detection 近期车道线检测论文阅读总结

车道线检测

End-to-end Lane Shape Prediction with Transformers

使用 Transformer 捕获道路中细长车道线特征和全局特征,所发明的车道线检测算法与以往相比,可端到端训练、参数量更少、速度更快(高达 420 fps,单 1080Ti)。

- 作者 | Ruijin Liu, Zejian Yuan, Tie Liu, Zhiliang Xiong
- 单位 | 西安交通大学;首都师范大学等
- 论文 | https://arxiv.org/abs/2011.04233
- 代码 | https://github.com/liuruijin17/LSTR
- 详解 | Transformer 又立功了!又快(420 fps)又好的车道线检测算法

Datasets 📂

Citation

Use this bibtex to cite this repository:

@misc{Awesome-Lane-Detection,
  title={Awesome-Lane-Detection: Some Works of Lane Detection},
  author={Charmve},
  year={2020.09},
  publisher={Github},
  journal={GitHub repository},
  howpublished={\url{https://github.com/Charmve/Awesome-Lane-Detection}},
}

Contact & Feedback :octocat:

If you have any suggestions about papers, feel free to mail me :) 📧 [email protected]