Monocular Lane Detection Based on Deep Learning: A Survey

Bingke Zhu; Haiyun Guo; Jianwu Fang; Jinqiao Wang; Kuan Zhu; Xin He; Xu Zhao

REVIEW 2 major objections 6 minor 1 cited by

Monocular Lane Detection Based on Deep Learning: A Survey

T0 review · 2 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A survey of deep-learning lane detection organizes every method around four core design choices.

desk verdict Useful 2D+3D lane detection survey with a genuinely new taxonomy; the 'unified' FPS table is the one place that needs an honest fix. read the letter →

arxiv 2411.16316 v6 pith:S63QYSD6 submitted 2024-11-25 cs.CV

Xin He , Haiyun Guo , Kuan Zhu , Bingke Zhu , Xu Zhao , Jianwu Fang , Jinqiao Wang This is my paper · ORCID

classification cs.CV

keywords lanedetectionmonocular3Ddeeplearningsurveyautonomousdrivingperceptionbird's-eyeviewinverseperspectivemappingmodelinginstancediscrimination

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey tries to establish that all deep-learning monocular lane detection methods, both 2D and 3D, can be understood through four core design axes: task paradigm (how lane instances are told apart), lane modeling (how a lane is encoded as learnable parameters), global context supplementation (how obscure lanes are inferred), and perspective effect elimination (how 3D lanes are obtained). It argues that this four-axis view is more useful than earlier surveys that focused on network structures and loss functions. It also claims to deliver the first survey covering both 2D and 3D lane detection together, along with a unified efficiency comparison of representative methods. A sympathetic reader would care because a correct taxonomy tells practitioners which design choices actually drive accuracy, speed, and usability for downstream planning.

What carries the argument

The central organizing device is the four-axis decomposition of lane detection algorithms. The axes are: instance-level discrimination (two-stage segmentation-based versus one-stage object detection-based), lane modeling (mask, grids, keypoints, line anchors, or curves), global context supplementation (special modules for obscure lanes), and perspective effect elimination (IPM, learnable BEV transformation, depth-based projection, or direct 3D modeling). This decomposition carries the survey's argument by providing the classification structure for all reviewed methods, the categories used in the benchmark tables, and the framework for the efficiency comparison and the discussion of empirical recipes.

What would settle it

Re-run the open-source methods from the efficiency table at a single input resolution with identical post-processing on the same GPU; if the FPS ordering changes substantially, the unified efficiency claim is weakened. Also, a method that achieves state-of-the-art results on CULane or OpenLane while fitting none of the four proposed axes would count against the taxonomy.

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Extended reading notes

Core claim

On the paper's own terms, the central claim is that the design space of monocular lane detection is spanned by four decisions. The task paradigm determines whether instance-level discrimination happens before, after, or alongside lane localization. Lane modeling determines whether a lane is represented as a mask, a grid of row-wise classifications, a set of keypoints, a line anchor, or a parametric curve. Global context supplementation covers mechanisms added to infer lanes that are occluded, shadowed, or otherwise visually weak. Perspective effect elimination covers how the camera's perspective distortion is handled, either through inverse perspective mapping under a flat-ground assumption, learnable front-view-to-BEV transformation, depth-based projection, or direct prediction of 3D lanes. The paper asserts that these four axes give a general pipeline for each method family, make 2D and 3D methods comparable, and are supported by the benchmark and speed results it reports.

Load-bearing premise

The survey's comparisons assume that accuracy numbers taken from the original papers, and FPS numbers measured on one GPU at each method's own input resolution and post-processing pipeline, are comparable across methods.

Editorial extensions

If this is right

If the four-axis view is right, a new lane detector can be described and positioned by where it falls on each axis, without needing to know its network architecture or loss function.
The flat-ground assumption of inverse perspective mapping is identified as the main weakness of 2D-to-3D projection, so methods that model 3D lanes directly avoid an error source that accurate 2D detection cannot fix.
Segmentation-based paradigms pay a clear efficiency cost for instance discrimination compared with one-stage object detection-based paradigms.
Supplementing global context is a near-universal design choice, and the survey associates its absence with weaker performance on occluded or otherwise obscured lanes.
State-of-the-art results on the OpenLane benchmark currently come from BEV-free methods that model 3D lanes directly rather than through a BEV feature intermediate.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference: the four-axis taxonomy likely transfers to adjacent road-perception tasks such as online HD map construction, where map elements also need instance discrimination, parametric modeling, global context, and geometric projection.
Editorial inference: read together with the benchmark tables, the unified FPS tests imply that speed numbers from original papers are not reliable for method selection, and a standardized resolution-plus-post-processing protocol would be needed for deployment decisions.
Editorial inference: because line-anchor and grid-based models struggle with U-shaped and near-horizontal lanes, a shift toward mask-based or curve-based modeling is plausible as multi-camera surround-view lane detection grows.
Editorial inference: the convergence of top 3D methods on BEV-free direct modeling suggests that camera-pose estimation and 3D lane modeling are becoming coupled in one network, which could eventually make IPM-based pipelines obsolete.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Here's the short version: this survey is worth sending to referees. It is the first to my knowledge to put 2D and 3D monocular lane detection under one taxonomy, and the four axes—task paradigm, lane modeling, global context supplementation, perspective effect elimination—are a real organizational advance, not just repackaged section headings. The benchmark tables cite original papers, the coverage runs through 2024, and the extended-works sections (video, HD map, topology reasoning) make it a useful one-stop map for someone entering the field or choosing baselines. The maintained GitHub repo is a plus.

The main flaw is the efficiency comparison. The abstract and Contribution 3 say the authors 'investigate their inference speed under a unified setting for fair comparison,' but Table VI is not as unified as advertised. Three entries are borrowed from other people's reimplementations (SCNN and LSTR from Feng et al., 3D-LaneNet from Guo et al.), and input resolutions range from 288x800 to 720x960 with post-processing ranging from none to NMS or clustering. The FPS numbers are therefore only loosely comparable, and Section V-C draws general lessons ('segmentation-based methods are less efficient', 'UFLD is fastest') from that table. The qualitative conclusions probably survive—the differences are large enough—but the precise rankings and the 'unified setting' language overstate the evidence. This is a moderate, fixable problem: either re-run everything at matched resolutions without post-processing, or table the numbers with clear provenance and caveats.

The camera-projection math is standard and correct. There is no new experimental claim beyond the FPS re-test, which is what makes the overclaim there matter more than it would in a research paper. The 'first survey covering both 2D and 3D lane detection' claim is plausible given the cited prior surveys focus on one side. The criteria for selecting representative methods could be stated more explicitly, but that is minor.

Bottom line: the taxonomy alone justifies engagement. With the efficiency section cleaned up, this would be a solid reference. I would send it to referees now and ask for a revision on the speed comparison.

Referee Report

2 major / 6 minor

Summary. This manuscript surveys deep-learning-based monocular lane detection, covering both 2D and 3D approaches. Its main organizational contribution is a four-axis taxonomy: task paradigm (instance-level discrimination), lane modeling (representing lanes as learnable parameters), global context supplementation (handling obscure lanes), and perspective effect elimination (obtaining accurate 3D lanes). The paper describes datasets and metrics, reviews representative 2D and 3D methods under this taxonomy, reports benchmark numbers on CULane, OpenLane, and other datasets, presents an efficiency comparison on a single RTX 3090 (Table VI), and adds chapters on extended tasks (multi-task perception, video lane detection, online HD map construction, lane topology reasoning) and future directions.

Significance. If the taxonomy is accepted, the survey provides a useful organizing framework that goes beyond the narrower structure of earlier surveys [30–32] by jointly covering 2D and 3D lane detection and by stressing design choices that matter for downstream use. The benchmark tables are a convenient reference and are transparently traced to the original papers, and the companion GitHub collection of papers and codes is a practical asset for the community. The efficiency comparison in Section V-B is a good-faith attempt to add an original empirical contribution, but, as detailed below, the claim that it is a 'unified setting' is stronger than the protocol supports. The survey does not introduce fitted quantities or predictions, so there is no circular-reasoning concern; the main risk is that readers may over-interpret the efficiency ranking and the cross-paper benchmark numbers.

major comments (2)

[§V-B, Table VI; Contribution 3 in §I] The claim of a 'unified environment' for fair efficiency comparison is not fully supported by the protocol. The table mixes input resolutions from 288×800 to 720×960, post-processing that ranges from none to NMS to clustering, and three entries are borrowed from other groups' reimplementations rather than run by the authors: SCNN and LSTR use Feng et al.'s reimplementation [20], and 3D-LaneNet uses Guo et al.'s reproduction [23]. Since FPS is measured as pure model inference without preprocessing or post-processing, the ranking cannot be read as an end-to-end deployment comparison. Section V-C then uses these numbers to ground practical guidance (e.g., that segmentation-based methods are less efficient, that UFLD is the fastest). I recommend either rerunning the speed tests at matched input resolutions with the required post-processing included, or explicitly reframing Table VI as per-configuration reference numbers and softening the corresponding statements in the abstract, Contribution 3, and Section V-C.
[§V-A, Tables IV and V] The benchmark tables present numbers collected from different original papers side by side, and readers may infer that the F1 values are directly comparable across rows. The paper describes the standard metrics in Section II-B but does not state that evaluation details can differ across papers (for example, matching thresholds, possible test-time augmentation, or ensemble configurations). I ask the authors to add an explicit caveat that reported numbers are copied as published and that small cross-row differences should be interpreted with caution. This does not undermine the survey's taxonomy, but it is part of the paper's empirical contribution.

minor comments (6)

[Table I, LLAMAS row] The table marks both '2D' and '3D' for LLAMAS, but Section II-A1 discusses LLAMAS among the 2D lane detection datasets; please clarify whether 3D annotations exist or remove the 3D checkmark.
[Table III, CLGo row] CLGo is cited as [23], but the text attributes this method to Liu et al. [131] (AAAI'22); the citation should be corrected for consistency with Table VIII.
[Table VII] LaneAF appears in both the segmentation-based and the object detection-based blocks; please remove the duplicate or indicate its intended category.
[§II-A2] The sentence 'The Apollo 3DLane dataset [23] is generated using the game engine' cites Gen-LaneNet [23], but the dataset was introduced in the 3D-LaneNet paper [22]; please update the citation.
[§I and §III-B1 and §IV-C] There are several typos: 'The sturcture of this paper' in Section I, a stray 't' after 'segmentation process.' in Section III-B1, and 'Derictly Modeling 3D Lanes' in Section IV-C.
[Table VI and text] The spelling of Gen-LaneNet is inconsistent ('GenLaneNet' in Table V and 'Gen-LaneNet' elsewhere), and the BézierLaneNet row contains a stray space in the method name.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: survey organizes externally reported results; no derivation reduces to its inputs.

full rationale

This is a literature survey, not a derivation paper, so the circularity burden is minimal. The four-axis taxonomy (task paradigm, lane modeling, global context supplementation, perspective effect elimination) is an interpretive organization of existing methods; it is not derived from a fitted quantity, from a self-citation, or from any equation of the paper. Benchmark tables in Section V-A are explicitly compiled 'from the data in the original paper,' so the reported F1/AP values are external, independently published results rather than predictions of this survey. The efficiency comparison in Section V-B is an original experimental measurement of inference FPS on one GPU, not a fitted parameter renamed as a prediction; the Table VI caption transparently discloses that SCNN and LSTR follow Feng et al.'s reimplementation [20] and that 3D-LaneNet was tested via Guo et al.'s reproduction [23] because the original is closed-source. Reproducing external baselines is not circular: the comparison does not assume the survey's own conclusions. No uniqueness theorem, ansatz, or definitional equivalence is invoked to force the four-axis categorization, and no load-bearing claim reduces to a self-citation. The skeptic's concern about mixed input resolutions and varying post-processing in the FPS comparison is a validity caveat about fairness of the empirical comparison, not circularity. Search for self-definitional, fitted-input-called-prediction, load-bearing self-citation, imported-uniqueness, ansatz-smuggling, or renaming patterns found no quoted equation or claim that is equivalent to its own input. Accordingly the circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

A survey introduces no free parameters or entities. The axioms listed are the implicit assumptions behind its taxonomy and benchmark comparisons.

assumptions (3)

domain assumption The four-axis design framework (task paradigm, lane modeling, global context supplementation, perspective effect elimination) is a sufficient organizing principle for the lane detection literature.
Introduced in Section I and used to structure Sections III and IV; this is an editorial choice, not a proven fact.
domain assumption Benchmark numbers reported in Tables IV, V, VII, VIII, IX are accurate transcriptions from the original papers.
Section V-A states results are 'derived from the data in the original paper'; mis-transcription would weaken the comparisons without affecting the taxonomy.
domain assumption The efficiency test on a single RTX 3090 at per-method input sizes provides a fair cross-method speed comparison.
Section V-B describes the setup; different resolutions and post-processing steps make the comparison approximate.

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Cite this review

Pith. "Pith review of Monocular Lane Detection Based on Deep Learning: A Survey." pith.science (2026). https://pith.science/paper/S63QYSD6

@misc{pith2026241116316,
  author       = {Pith},
  title        = {Pith review of: Monocular Lane Detection Based on Deep Learning: A Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S63QYSD6}},
  note         = {Machine review of arXiv:2411.16316}
}

read the original abstract

Lane detection plays an important role in autonomous driving perception systems. As deep learning algorithms gain popularity, monocular lane detection methods based on them have demonstrated superior performance and emerged as a key research direction in autonomous driving perception. The core designs of these algorithmic frameworks can be summarized as follows: (1) Task paradigm, focusing on lane instance-level discrimination; (2) Lane modeling, representing lanes as a set of learnable parameters in the neural network; (3) Global context supplementation, enhancing inference on the obscure lanes; (4) Perspective effect elimination, providing accurate 3D lanes for downstream applications. From these perspectives, this paper presents a comprehensive overview of existing methods, encompassing both the increasingly mature 2D lane detection approaches and the developing 3D lane detection works. Besides, this paper compares the performance of mainstream methods on different benchmarks and investigates their inference speed under a unified setting for fair comparison. Moreover, we present some extended works on lane detection, including multi-task perception, video lane detection, online high-definition map construction, and lane topology reasoning, to offer readers a comprehensive roadmap for the evolution of lane detection. Finally, we point out some potential future research directions in this field. We exhaustively collect the papers and codes of existing works at https://github.com/Core9724/Awesome-Lane-Detection and will keep tracing the research.

Figures

Figures reproduced from arXiv: 2411.16316 by the authors.

**Figure 2.** The structure of this paper. Different colors represent specific sections. The summary of existing methods covers four [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** The general pipelines for 2D lane detection: (a) Segmentation-based methods, which leverage mask, grids, or keypoints [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (5 more)

**Figure 5.** Figure 5: More details of different lane modeling. (a) A lane [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: Illustration of the errors introduced by IPM on the [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]

**Figure 7.** Figure 7: The general pipeline of 3D lane detection. (a) BEV-based methods. The core is the view transformation from FV [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]

**Figure 8.** Figure 8: The roadmap for the development of hotspots in lane detection research. In each domain, a symbolic event is identified [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]

**Figure 9.** Figure 9: Geometry setup about camera and 3D lane. The [PITH_FULL_IMAGE:figures/full_fig_p019_9.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

LanePerf: a Performance Estimation Framework for Lane Detection
cs.CV 2025-07 conditional novelty 6.0 of 10

LanePerf estimates lane-detection F1 on unlabeled target domains by fusing CLIP image features with lane features, achieving MAE 0.117 and Spearman's rho 0.727 on OpenLane.

Reference graph

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Reviewed August 12, 2026 · model on record in the stance chip above.

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