# Ultra Fast Deep Lane Detection with Hybrid Anchor Driven Ordinal Classification

arXiv: https://arxiv.org/abs/2206.07389 · HTML: https://mlsanity.com/paper/2206.07389/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 69 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 30 of 42)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [Spatial As Deep: Spatial CNN for Traffic Scene Understanding](https://mlsanity.com/paper/1712.06080/) (2017)
- [FCOS: A simple and strong anchor-free object detector](https://mlsanity.com/paper/2006.09214/) (2020)
- [Learning Lightweight Lane Detection CNNs by Self Attention Distillation](https://mlsanity.com/paper/1908.00821/) (2019)
- [Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection](https://mlsanity.com/paper/2010.12035/) (2020)
- [Towards End-to-End Lane Detection: an Instance Segmentation Approach](https://mlsanity.com/paper/1802.05591/) (2018)
- [Ultra Fast Structure-aware Deep Lane Detection](https://mlsanity.com/paper/2004.11757/) (2020)
- [End-to-end Lane Shape Prediction with Transformers](https://mlsanity.com/paper/2011.04233/) (2020)
- [3D-LaneNet: End-to-End 3D Multiple Lane Detection](https://mlsanity.com/paper/1811.10203/) (2018)
- [PolyLaneNet: Lane Estimation via Deep Polynomial Regression](https://mlsanity.com/paper/2004.10924/) (2020)
- [CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution](https://mlsanity.com/paper/2105.05003/) (2021)
- [CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending](https://mlsanity.com/paper/2007.12147/) (2020)
- [RESA: Recurrent Feature-Shift Aggregator for Lane Detection](https://mlsanity.com/paper/2008.13719/) (2020)
- [Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection](https://mlsanity.com/paper/2003.10656/) (2020)
- [Focus on Local: Detecting Lane Marker from Bottom Up via Key Point](https://mlsanity.com/paper/2105.13680/) (2021)
- [Inter-Region Affinity Distillation for Road Marking Segmentation](https://mlsanity.com/paper/2004.05304/) (2020)
- [VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition](https://mlsanity.com/paper/1710.06288/) (2017)

## Built on by (most cited first, 30 of 39)

- [LATR: 3D Lane Detection from Monocular Images with Transformer](https://mlsanity.com/paper/2308.04583/) (2023)
- [Towards Robust Physical-world Backdoor Attacks on Lane Detection](https://mlsanity.com/paper/2405.05553/) (2024)
- [LanEvil: Benchmarking the Robustness of Lane Detection to Environmental Illusions](https://mlsanity.com/paper/2406.00934/) (2024)
- [CLRerNet: Improving Confidence of Lane Detection with LaneIoU](https://mlsanity.com/paper/2305.08366/) (2023)
- [ADNet: Lane Shape Prediction via Anchor Decomposition](https://mlsanity.com/paper/2308.10481/) (2023)
- [BSNet: Lane Detection via Draw B-spline Curves Nearby](https://mlsanity.com/paper/2301.06910/) (2023)
- [TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars](https://mlsanity.com/paper/2307.10705/) (2023)
- [Recurrent Generic Contour-based Instance Segmentation with Progressive Learning](https://mlsanity.com/paper/2301.08898/) (2023)
- [Monocular 3D lane detection for Autonomous Driving: Recent Achievements, Challenges, and Outlooks](https://mlsanity.com/paper/2404.06860/) (2024)
- [Anchor3DLane++: 3D Lane Detection via Sample-Adaptive Sparse 3D Anchor Regression](https://mlsanity.com/paper/2412.16889/) (2024)
- [TopoMaskV2: Enhanced Instance-Mask-Based Formulation for the Road Topology Problem](https://mlsanity.com/paper/2409.11325/) (2024)
- [Robust Lane Detection through Self Pre-training with Masked Sequential Autoencoders and Fine-tuning with Customized PolyLoss](https://mlsanity.com/paper/2305.17271/) (2023)
- [BezierFormer: A Unified Architecture for 2D and 3D Lane Detection](https://mlsanity.com/paper/2404.16304/) (2024)
- [TopoBDA: Towards Bezier Deformable Attention for Road Topology Understanding](https://mlsanity.com/paper/2412.18951/) (2024)
- [Dense Hybrid Proposal Modulation for Lane Detection](https://mlsanity.com/paper/2304.14874/) (2023)
- [Poisson Ordinal Network for Gleason Group Estimation Using Bi-Parametric MRI](https://mlsanity.com/paper/2407.05796/) (2024)
- [Monocular Lane Detection Based on Deep Learning: A Survey](https://mlsanity.com/paper/2411.16316/) (2024)
- [BADAS: Context Aware Collision Prediction Using Real-World Dashcam Data](https://mlsanity.com/paper/2510.14876/) (2025)
- [Sketch and Refine: Towards Fast and Accurate Lane Detection](https://mlsanity.com/paper/2401.14729/) (2024)
- [CLRKDNet: Speeding up Lane Detection with Knowledge Distillation](https://mlsanity.com/paper/2405.12503/) (2024)
- [LaneTCA: Enhancing Video Lane Detection with Temporal Context Aggregation](https://mlsanity.com/paper/2408.13852/) (2024)
- [GLane3D : Detecting Lanes with Graph of 3D Keypoints](https://mlsanity.com/paper/2503.23882/) (2025)
- [Contrastive Learning for Lane Detection via cross-similarity](https://mlsanity.com/paper/2308.08242/) (2023)
- [DALNet: A Rail Detection Network Based on Dynamic Anchor Line](https://mlsanity.com/paper/2308.11381/) (2023)
- [Rethinking Lanes and Points in Complex Scenarios for Monocular 3D Lane Detection](https://mlsanity.com/paper/2503.06237/) (2025)
- [FENet: Focusing Enhanced Network for Lane Detection](https://mlsanity.com/paper/2312.17163/) (2023)
- [LOID: Lane Occlusion Inpainting and Detection for Enhanced Autonomous Driving Systems](https://mlsanity.com/paper/2408.09117/) (2024)
- [Polar R-CNN: End-to-End Lane Detection with Fewer Anchors](https://mlsanity.com/paper/2411.01499/) (2024)
- [Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective](https://mlsanity.com/paper/2607.05783/) (2026)
- [LineMarkNet: Line Landmark Detection for Valet Parking](https://mlsanity.com/paper/2309.10475/) (2023)

## Abstract (arXiv)

Modern methods mainly regard lane detection as a problem of pixel-wise segmentation, which is struggling to address the problems of efficiency and challenging scenarios like severe occlusions and extreme lighting conditions. Inspired by human perception, the recognition of lanes under severe occlusions and extreme lighting conditions is mainly based on contextual and global information. Motivated by this observation, we propose a novel, simple, yet effective formulation aiming at ultra fast speed and the problem of challenging scenarios. Specifically, we treat the process of lane detection as an anchor-driven ordinal classification problem using global features. First, we represent lanes with sparse coordinates on a series of hybrid (row and column) anchors. With the help of the anchor-driven representation, we then reformulate the lane detection task as an ordinal classification problem to get the coordinates of lanes. Our method could significantly reduce the computational cost with the anchor-driven representation. Using the large receptive field property of the ordinal classification formulation, we could also handle challenging scenarios. Extensive experiments on four lane detection datasets show that our method could achieve state-of-the-art performance in terms of both speed and accuracy. A lightweight version could even achieve 300+ frames per second(FPS). Our code is at https://github.com/cfzd/Ultra-Fast-Lane-Detection-v2.