# End-to-End Lane Marker Detection via Row-wise Classification

arXiv: https://arxiv.org/abs/2005.08630 · HTML: https://mlsanity.com/paper/2005.08630/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2020.

## Built on by (most cited first, 30 of 47)

- [CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution](https://mlsanity.com/paper/2105.05003/) (2021)
- [Rethinking Efficient Lane Detection via Curve Modeling](https://mlsanity.com/paper/2203.02431/) (2022)
- [CLRNet: Cross Layer Refinement Network for Lane Detection](https://mlsanity.com/paper/2203.10350/) (2022)
- [A Keypoint-based Global Association Network for Lane Detection](https://mlsanity.com/paper/2204.07335/) (2022)
- [Focus on Local: Detecting Lane Marker from Bottom Up via Key Point](https://mlsanity.com/paper/2105.13680/) (2021)
- [Online Map Vectorization for Autonomous Driving: A Rasterization Perspective](https://mlsanity.com/paper/2306.10502/) (2023)
- [LATR: 3D Lane Detection from Monocular Images with Transformer](https://mlsanity.com/paper/2308.04583/) (2023)
- [LaneAF: Robust Multi-Lane Detection with Affinity Fields](https://mlsanity.com/paper/2103.12040/) (2021)
- [MAN TruckScenes: A multimodal dataset for autonomous trucking in diverse conditions](https://mlsanity.com/paper/2407.07462/) (2024)
- [Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints](https://mlsanity.com/paper/2112.15351/) (2021)
- [RCLane: Relay Chain Prediction for Lane Detection](https://mlsanity.com/paper/2207.09399/) (2022)
- [LanEvil: Benchmarking the Robustness of Lane Detection to Environmental Illusions](https://mlsanity.com/paper/2406.00934/) (2024)
- [ADNet: Lane Shape Prediction via Anchor Decomposition](https://mlsanity.com/paper/2308.10481/) (2023)
- [GroupLane: End-to-End 3D Lane Detection with Channel-wise Grouping](https://mlsanity.com/paper/2307.09472/) (2023)
- [Robust Lane Detection via Expanded Self Attention](https://mlsanity.com/paper/2102.07037/) (2021)
- [Monocular 3D lane detection for Autonomous Driving: Recent Achievements, Challenges, and Outlooks](https://mlsanity.com/paper/2404.06860/) (2024)
- [Rail Detection: An Efficient Row-based Network and A New Benchmark](https://mlsanity.com/paper/2304.05667/) (2023)
- [Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Survey](https://mlsanity.com/paper/2304.10891/) (2023)
- [Lane Detection with Versatile AtrousFormer and Local Semantic Guidance](https://mlsanity.com/paper/2203.04067/) (2022)
- [Towards Driving-Oriented Metric for Lane Detection Models](https://mlsanity.com/paper/2203.16851/) (2022)
- [BEV-LaneDet: a Simple and Effective 3D Lane Detection Baseline](https://mlsanity.com/paper/2210.06006/) (2022)
- [A Hybrid Spatial-temporal Deep Learning Architecture for Lane Detection](https://mlsanity.com/paper/2110.04079/) (2021)
- [Dense Hybrid Proposal Modulation for Lane Detection](https://mlsanity.com/paper/2304.14874/) (2023)
- [HoughLaneNet: Lane Detection with Deep Hough Transform and Dynamic Convolution](https://mlsanity.com/paper/2307.03494/) (2023)
- [ALINA: Advanced Line Identification and Notation Algorithm](https://mlsanity.com/paper/2406.08775/) (2024)
- [HeightLane: BEV Heightmap guided 3D Lane Detection](https://mlsanity.com/paper/2408.08270/) (2024)
- [Monocular Lane Detection Based on Deep Learning: A Survey](https://mlsanity.com/paper/2411.16316/) (2024)
- [On Robustness of Lane Detection Models to Physical-World Adversarial Attacks in Autonomous Driving](https://mlsanity.com/paper/2107.02488/) (2021)
- [GLane3D : Detecting Lanes with Graph of 3D Keypoints](https://mlsanity.com/paper/2503.23882/) (2025)
- [SwiftLane: Towards Fast and Efficient Lane Detection](https://mlsanity.com/paper/2110.11779/) (2021)

## Abstract (arXiv)

In autonomous driving, detecting reliable and accurate lane marker positions is a crucial yet challenging task. The conventional approaches for the lane marker detection problem perform a pixel-level dense prediction task followed by sophisticated post-processing that is inevitable since lane markers are typically represented by a collection of line segments without thickness. In this paper, we propose a method performing direct lane marker vertex prediction in an end-to-end manner, i.e., without any post-processing step that is required in the pixel-level dense prediction task. Specifically, we translate the lane marker detection problem into a row-wise classification task, which takes advantage of the innate shape of lane markers but, surprisingly, has not been explored well. In order to compactly extract sufficient information about lane markers which spread from the left to the right in an image, we devise a novel layer, which is utilized to successively compress horizontal components so enables an end-to-end lane marker detection system where the final lane marker positions are simply obtained via argmax operations in testing time. Experimental results demonstrate the effectiveness of the proposed method, which is on par or outperforms the state-of-the-art methods on two popular lane marker detection benchmarks, i.e., TuSimple and CULane.