LanePerf: a Performance Estimation Framework for Lane Detection

Ahmed Abouelazm; Christian Hubschneider; Daniel Slieter; J. Marius Z\"ollner; Yin Wu

REVIEW 3 major objections 6 minor 31 references

LanePerf: a Performance Estimation Framework for Lane Detection

T0 review · 3 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A two-branch estimator predicts lane-detection F1 scores on unlabeled video, cutting error below five adapted baselines.

desk verdict A sensible first AutoEval method for lane detection, but the claimed edge over the simplest baseline needs significance tests and per-domain numbers before it can be trusted. read the letter →

arxiv 2507.12894 v1 pith:HT6XDJ27 submitted 2025-07-17 cs.CV

Yin Wu , Daniel Slieter , Ahmed Abouelazm , Christian Hubschneider , J. Marius Zöllner This is my paper

classification cs.CV

keywords lanedetectionperformanceestimationdomainshiftAutoEvalout-of-distributionzero-lanescenarioCLIPfeaturesDeepSets

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

The paper argues that lane-detection performance under domain shift can be estimated without ground-truth labels by combining general image features with lane-specific features. It shows that AutoEval methods borrowed from image classification fail on lane detection because the output is a variable-length set of lanes, often empty, and detector confidence degrades under shift. LanePerf addresses this with a pretrained image encoder plus a DeepSets aggregator for lane features, including a learnable placeholder for zero-lane outputs. On the OpenLane benchmark, across scene, weather, and hour shifts, it reports the lowest MAE (0.117) and highest Spearman's rho (0.727) among six methods. If these numbers hold, ADAS teams could monitor deployed lane detectors on unlabeled fleet data and flag risky segments without annotation costs.

What carries the argument

The central object is a two-branch fusion estimator: a frozen pretrained image encoder (CLIP ViT-B/32) produces a domain-general scene representation, while a DeepSets architecture turns the variable-length set of per-lane feature vectors into a fixed-size representation via a two-layer fully connected network and mean pooling. When the lane detector returns an empty set, a learnable default lane feature substitutes, letting the network still predict a score. The concatenated image and lane representations pass through a second fully connected network with a sigmoid output, producing an estimated F1 in [0,1] that is trained on labeled source-domain validation mini-datasets.

What would settle it

Recompute MAE and Spearman's rho on the same OpenLane segments with a block bootstrap over segments, and check whether LanePerf's 0.117 vs 0.130 MAE and 0.727 vs 0.669 rho advantages over Average Confidence survive at a 95% confidence interval; also measure calibration on an independent set of no-lane mini-datasets to test the zero-lane placeholder directly.

Watch

Extended reading notes

Core claim

LanePerf estimates the F1 score of a frozen lane-detection model (CLRNet) on unlabeled target-domain mini-datasets by concatenating image-level features from a pretrained CLIP ViT-B/32 encoder with lane-level features aggregated by a DeepSets module through mean pooling. A learnable default lane feature acts as a placeholder when the detector outputs no lanes, so the estimator remains defined in zero-lane scenarios. On ten cross-domain transfer settings within OpenLane, LanePerf achieves MAE 0.117 and Spearman rho 0.727, outperforming Average Confidence, DOC, ATC, FID, and Energy-based baselines, and ranks first on eight of ten evaluations. An ablation confirms that combining image and lane features gives lower and more consistent error than either alone, and a reduced-size experiment shows it tracks actual F1 even when no lanes are present.

Load-bearing premise

The evaluation treats each approximately 200-frame consecutive video segment as an independent measurement when computing MAE and Spearman's rho, but the paper does not report the number of segments per domain and consecutive frames are autocorrelated, so the reported advantage over baselines could shrink if the effective sample size is small.

Editorial extensions

If this is right

If the reported results hold, lane-detection systems can be monitored on unlabeled target-domain video segments, with low predicted F1 flagging potential corner cases for closer inspection.
Because the image encoder is swappable (CLIP, ViT, and DINOv2 give similar results), the framework can incorporate better foundation models without redesign.
The zero-lane placeholder lets performance estimation remain meaningful in empty-output scenarios, which break confidence-based baselines like Average Confidence.
The ablation indicates that image features carry the domain-shift signal that lane features miss, pointing to scene semantics as a key driver of the improvement.

Reading between the lines

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

Extending beyond the paper: the same two-branch design – a general scene encoder plus a permutation-invariant aggregator for a variable-length output set – could transfer to other structured-output perception tasks such as object detection or multi-object tracking, where empty predictions are also meaningful.
A testable extension would be to apply LanePerf to other lane detectors (e.g., parameter-based or segmentation-based models) to see whether the estimator calibrates to each detector's feature space or transfers across detectors; the paper only uses CLRNet.
Because the mini-datasets are consecutive frames, a bootstrapped confidence interval over segments would clarify whether the 0.117 vs 0.130 MAE gap over Average Confidence is statistically robust; the paper does not report this.
The strong weather-domain results suggest an even simpler variant might work for specific shifts – using only image features plus a shift-type indicator – but the paper shows the fusion is needed for overall consistency.

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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

The paper does something genuinely new: it brings label-free performance estimation to lane detection, a task where the variable number of outputs and zero-lane cases break the usual classification assumptions. Adapting five existing methods as baselines is useful, and LanePerf's design is sane — a frozen CLIP encoder for scene-level features, a DeepSets aggregator for the variable-size lane set, and a learnable placeholder for the empty set. The zero-lane idea is the most interesting bit, and the ablations on image encoder choice (CLIP vs ViT vs DINOv2) are a plus. The problem is real and the paper is easy to follow.

The soft spots are all in the evidence. Table II gives MAE and Spearman's rho without reporting how many mini-datasets per target domain, without confidence intervals, and without any paired test. Since each mini-dataset is ~200 consecutive frames, the effective sample size is far below the raw count; the reported rho values may be stable-looking but have wide uncertainty. The stress-test note is right: the overall gain over AC (MAE 0.117 vs 0.130, rho 0.727 vs 0.669) is modest, and per-domain the picture is not uniformly favorable — Highway goes against LanePerf (MAE 0.103 vs 0.074, rho 0.591 vs 0.713) and Night MAE is worse (0.113 vs 0.098). So the Section I claim of "significantly outperforming" is not supported by the statistics as presented. Also, the zero-lane validation is a single qualitative example, not a quantitative evaluation over many empty-set cases. One dataset and one MuT further limits the generality claim.

What is not a problem: circularity. All fitted components, including the ATC threshold, the regressors, and LanePerf itself, are fit on source validation data only; target labels are held out. That part is clean.

Who is this for? People building ADAS testing pipelines and anyone working on AutoEval for structured outputs. It deserves a serious referee, not a desk reject — the problem selection is good and the architecture is a reasonable starting point. But I would send it back for a major revision: report N per domain, add bootstrap or permutation intervals for MAE and rho, run a proper paired significance test against AC, quantify zero-lane performance across many mini-datasets, and release code and data splits. With those fixes the claim would be easy to verify; without them it is a set of plausible but unproven point estimates.

Referee Report

3 major / 6 minor

Summary. The paper adapts five image-classification performance-estimation baselines (AC, DOC, ATC, FID, EBM) to lane detection and proposes LanePerf, which concatenates CLIP image features with DeepSets-aggregated lane features and a learnable default lane feature for zero-lane inputs. Using CLRNet as the Model under Test on OpenLane, it evaluates MAE and Spearman's rho between estimated and actual F1 per mini-dataset under scene, weather, and hours domain shifts, reporting an overall MAE of 0.117 and rho of 0.727. The central claim is that LanePerf significantly outperforms all baselines.

Significance. The problem is relevant and the paper is, to my knowledge, the first systematic adaptation of AutoEval methods to lane detection. The proposed combination of a frozen pretrained image encoder with permutation-invariant lane-feature aggregation is sensible, and the zero-lane handling via a learnable placeholder is a reasonable design. The paper provides adapted baselines, ablations on image/lane features, and an ablation on encoder choice. However, the empirical case rests on point estimates without uncertainty quantification, so the strength of the claimed improvement over the best baseline is currently unverified.

major comments (3)

[Section V, Section VI, Table II, Eqs. (10)-(11)] The headline comparison lacks statistical support. The paper never reports the number of mini-datasets N per target domain, and each mini-dataset contains approximately 200 consecutive frames, so the effective number of independent evaluation units is much smaller than the raw count. The reported gaps are modest in absolute terms (overall MAE 0.117 vs 0.130; rho 0.727 vs 0.669), and per-domain results are not uniformly favorable (e.g., Highway MAE 0.103 vs AC 0.074; Night MAE 0.113 vs AC 0.098). Without bootstrap or permutation confidence intervals, or a paired significance test, the statement in Section I that LanePerf is 'significantly outperforming existing baselines' is not supported. Please report N per domain, the effective sample size after accounting for autocorrelation, and uncertainty intervals for MAE and rho, and test the LanePerf-vs-AC difference explicitly.
[Section III and Section V] The fitting protocol for the baseline regressors and for LanePerf is under-specified. For FID and EBM, Eqs. (7) and (9) refer to a regression model trained on 'a collection of validation datasets,' but the paper does not state how many mini-datasets are used, how the regression targets are computed, or how LanePerf's regressor is trained (loss, epochs, hyperparameters, and the exact split between fitting and evaluation mini-datasets). Without these details the results cannot be reproduced, and it is unclear whether all methods are evaluated on exactly the same evaluation units.
[Section VI-C and Figure 4] The zero-lane experiment is only qualitative. Figure 4 shows one example from Residential Area to Suburbs using 50-frame mini-datasets, but the paper reports no counts of zero-lane mini-datasets, no aggregate error comparison, and no significance test for this setting. The claim that LanePerf 'successfully leverages image-level information' needs quantitative support, especially because 50-frame segments are even more autocorrelated than the 200-frame segments used in Table II.

minor comments (6)

[Table II] The header 'EMB' should be 'EBM' to match the method name used in the text and in Eqs. (8)-(9).
[Section VI-B] The phrase 'Spearman's Spearman's rho' is a typo and should read 'Spearman's rho'.
[Section V, Metrics] Eqs. (10) and (11) use N for the number of mini-datasets, but N was used earlier for the number of samples in a dataset; please rename one of them to avoid confusion.
[Section VI-A] The statement that 'all methods degrade significantly in the Parking lot scenario' is not supported by any significance test; please soften to a descriptive claim or add intervals.
[Figure 4 caption] The caption says 'We show the first 200 subsets from the Suburbs split,' but the x-axis is labeled 'mini-dataset consisting of 50 images'; please clarify how many total 50-frame mini-datasets exist in that split and how these subsets were selected.
[Section VI-B, Night setting] The sentence 'it still achieves a higher Spearman's rho, indicating more reliable performance ranking across models' is imprecise: the rho is computed across mini-datasets, not across models, so the wording should be revised to describe ranking of mini-datasets.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: all fitted components are calibrated on source-domain validation data and evaluated on held-out target domains.

full rationale

The central derivation chain is self-contained. LanePerf and all baselines (AC, DOC, ATC, FID, EBM) fit their parameters—including the ATC threshold, FID/EBM linear regressors, LanePerf's DeepSets weights, and the learnable default lane feature—exclusively on source-domain validation datasets, which are drawn from the same distribution as the training data (Section III-A and Section V). Evaluation is performed on target-domain mini-datasets that are excluded from the training of both the lane detection model and the performance estimation model, and no target labels or target-derived actual F1 scores are used during fitting (Figure 2, Section V). The reported MAE and Spearman's rho are computed by comparing per-mini-dataset estimates against actually computed F1 scores on those held-out target domains, so the 'prediction' is not a renamed fit. The learnable default lane feature is a learned parameter, not a target-derived constant, and the zero-lane evaluation in Section VI-C uses held-out target subsets. The potential statistical weakness raised by the skeptical review—small effective sample size due to autocorrelated consecutive frames and missing per-domain mini-dataset counts—is a validity or significance concern, not a circularity concern, because it does not imply that any predicted quantity was an input to the fitting procedure. No self-citation chain, uniqueness theorem, or ansatz-smuggling-via-citation is used to force the framework's design. Accordingly, no circular step can be exhibited, and the score is 0.

Assumptions & free parameters 1 free parameters · 4 assumptions · 1 invented entities

The central claim rests on the transferability of pretrained image features and the source-validation-trained regressor, plus the validity of the OpenLane domain labels and the F1 metric. The learnable default lane feature is the only newly introduced entity; it has no independent evidence outside the paper's internal validation.

free parameters (1)

CLRNet confidence threshold = 0.4
Chosen following the original CLRNet implementation (Section V); it determines which predicted lanes are kept and thus the lane features and logits used by all estimators, including LanePerf.

assumptions (4)

domain assumption Pretrained CLIP ViT-B/32 features provide a domain-generalizable representation that remains informative for predicting lane-detection performance under domain shift.
This is the core assumption motivating the image branch in LanePerf, stated in Section IV-A and used throughout the framework.
domain assumption The relationship between image/lane features and F1 score learned on source-domain validation segments transfers to all target domains without recalibration.
This is inherent to the AutoEval setup (Section III-A) and is not validated across different training regimes or datasets.
domain assumption Ground-truth F1 scores computed with lane-level IoU threshold 0.5 are a faithful performance metric for lane detection.
Adopted from standard practice in Section III-A; the paper does not compare with alternative metrics or matching rules.
domain assumption The OpenLane dataset's domain annotations (scene, weather, hours) correctly partition the data into distinct distributions.
The evaluation relies on these labels to define source and target domains (Section V).

invented entities (1)

Learnable default lane feature
purpose: Placeholder for zero-lane outputs so the DeepSets module can process empty sets and the framework can still make predictions.
Introduced in Section IV-A; it is a learned vector parameter with no external falsifiable handle, only validated indirectly through overall performance.

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Cite this review

Pith. "Pith review of LanePerf: a Performance Estimation Framework for Lane Detection." pith.science (2026). https://pith.science/paper/HT6XDJ27

@misc{pith2026250712894,
  author       = {Pith},
  title        = {Pith review of: LanePerf: a Performance Estimation Framework for Lane Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HT6XDJ27}},
  note         = {Machine review of arXiv:2507.12894}
}

read the original abstract

Lane detection is a critical component of Advanced Driver-Assistance Systems (ADAS) and Automated Driving System (ADS), providing essential spatial information for lateral control. However, domain shifts often undermine model reliability when deployed in new environments. Ensuring the robustness and safety of lane detection models typically requires collecting and annotating target domain data, which is resource-intensive. Estimating model performance without ground-truth labels offers a promising alternative for efficient robustness assessment, yet remains underexplored in lane detection. While previous work has addressed performance estimation in image classification, these methods are not directly applicable to lane detection tasks. This paper first adapts five well-performing performance estimation methods from image classification to lane detection, building a baseline. Addressing the limitations of prior approaches that solely rely on softmax scores or lane features, we further propose a new Lane Performance Estimation Framework (LanePerf), which integrates image and lane features using a pretrained image encoder and a DeepSets-based architecture, effectively handling zero-lane detection scenarios and large domain-shift cases. Extensive experiments on the OpenLane dataset, covering diverse domain shifts (scenes, weather, hours), demonstrate that our LanePerf outperforms all baselines, achieving a lower MAE of 0.117 and a higher Spearman's rank correlation coefficient of 0.727. These findings pave the way for robust, label-free performance estimation in ADAS, supporting more efficient testing and improved safety in challenging driving scenarios.

Figures

Figures reproduced from arXiv: 2507.12894 by the authors.

**Figure 2.** The lane detection model is trained on the source [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Our model predicts F1 score for lane detection [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 5.** Figure 5: Ablation study on the influence of using image [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: Ablation study on the selection of pretrained image [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

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Works this paper leans on

31 extracted references · 23 canonical work pages

[1]

Lanenet: Real-time lane detection networks for autonomous driving,

Z. Wang, W. Ren, and Q. Qiu, “Lanenet: Real-time lane detection networks for autonomous driving,” arXiv preprint arXiv:1807.01726 , 2018

arXiv 2018
[2]

Spatial as deep: Spatial cnn for traffic scene understanding,

X. Pan, J. Shi, P. Luo, X. Wang, and X. Tang, “Spatial as deep: Spatial cnn for traffic scene understanding,” in Proceedings of the AAAI conference on artificial intelligence , 2018

work page 2018
[3]

Line-cnn: End-to-end traffic line detection with line proposal unit,

X. Li, J. Li, X. Hu, and J. Yang, “Line-cnn: End-to-end traffic line detection with line proposal unit,” IEEE Transactions on Intelligent Transportation Systems, 2019

work page 2019
[4]

Keep your eyes on the lane: Real-time attention- guided lane detection,

L. Tabelini, R. Berriel, T. M. Paixao, C. Badue, A. F. De Souza, and T. Oliveira-Santos, “Keep your eyes on the lane: Real-time attention- guided lane detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021

work page 2021
[5]

Polylanenet: Lane estimation via deep polynomial regression,

——, “Polylanenet: Lane estimation via deep polynomial regression,” in 2020 25th International Conference on Pattern Recognition (ICPR), 2021

work page 2020
[6]

Clrnet: Cross layer refinement network for lane detection,

T. Zheng, Y . Huang, Y . Liu, W. Tang, Z. Yang, D. Cai, and X. He, “Clrnet: Cross layer refinement network for lane detection,” in Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022

work page 2022
[7]

Qui ˜nonero-Candela, M

J. Qui ˜nonero-Candela, M. Sugiyama, A. Schwaighofer, and N. D. Lawrence, Dataset shift in machine learning . Mit Press, 2009

work page 2009
[8]

Are labels always necessary for classifier accuracy evaluation?

W. Deng and L. Zheng, “Are labels always necessary for classifier accuracy evaluation?” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021

work page 2021

Show all 31 references

[9]

A baseline for detecting misclassified and out-of-distribution examples in neural networks,

D. Hendrycks and K. Gimpel, “A baseline for detecting misclassified and out-of-distribution examples in neural networks,” arXiv preprint arXiv:1610.02136, 2016

2016 arXiv
[10]

Leveraging unlabeled data to predict out-of-distribution perfor- mance,

S. Garg, S. Balakrishnan, Z. C. Lipton, B. Neyshabur, and H. Sedghi, “Leveraging unlabeled data to predict out-of-distribution perfor- mance,” arXiv preprint arXiv:2201.04234 , 2022

2022 arXiv
[11]

Predicting with confidence on unseen distributions,

D. Guillory, V . Shankar, S. Ebrahimi, T. Darrell, and L. Schmidt, “Predicting with confidence on unseen distributions,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021

2021
[12]

A simple unified framework for detecting out-of-distribution samples and adversarial attacks,

K. Lee, K. Lee, H. Lee, and J. Shin, “A simple unified framework for detecting out-of-distribution samples and adversarial attacks,” Advances in neural information processing systems , 2018

2018
[13]

Out-of-distribution detection with deep nearest neighbors,

Y . Sun, Y . Ming, X. Zhu, and Y . Li, “Out-of-distribution detection with deep nearest neighbors,” in International Conference on Machine Learning, 2022

2022
[14]

Energy-based out-of- distribution detection,

W. Liu, X. Wang, J. Owens, and Y . Li, “Energy-based out-of- distribution detection,” Advances in neural information processing systems, 2020

2020
[15]

Your classifier is secretly an energy based model and you should treat it like one,

D. Duvenaud, J. Wang, J. Jacobsen, K. Swersky, M. Norouzi, and W. Grathwohl, “Your classifier is secretly an energy based model and you should treat it like one,” in ICLR 2020, 2020

2020
[16]

Monocular lane detection based on deep learning: A survey,

X. He, H. Guo, K. Zhu, B. Zhu, X. Zhao, J. Fang, and J. Wang, “Monocular lane detection based on deep learning: A survey,” arXiv preprint arXiv:2411.16316, 2024

2024 arXiv
[17]

Lane detection for autonomous driving: Com- prehensive reviews, current challenges, and future predictions,

J. Bi, Y . Song, Y . Jiang, L. Sun, X. Wang, Z. Liu, J. Xu, S. Quan, Z. Dai, and W. Yan, “Lane detection for autonomous driving: Com- prehensive reviews, current challenges, and future predictions,” IEEE Transactions on Intelligent Transportation Systems , 2025

2025
[18]

Unsupervised labeled lane markers using maps,

K. Behrendt and R. Soussan, “Unsupervised labeled lane markers using maps,” in Proceedings of the IEEE/CVF international conference on computer vision workshops , 2019

2019
[19]

Tusimple benchmark,

TuSimple, “Tusimple benchmark,” https://github.com/TuSimple/ tusimple-benchmark/, accessed: September 2020

2020
[20]

Bridging the gap of lane detection performance between different datasets: Unified viewpoint transformation,

T. Wen, D. Yang, K. Jiang, C. Yu, J. Lin, B. Wijaya, and X. Jiao, “Bridging the gap of lane detection performance between different datasets: Unified viewpoint transformation,” IEEE Transactions on Intelligent Transportation Systems, 2020

2020
[21]

Towards weakly-supervised domain adaptation for lane detection,

J. Zhou, C. Zhang, and J. Beyerer, “Towards weakly-supervised domain adaptation for lane detection,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024
[22]

On calibration of modern neural networks,

C. Guo, G. Pleiss, Y . Sun, and K. Q. Weinberger, “On calibration of modern neural networks,” in International conference on machine learning, 2017

2017
[23]

Agreement- on-the-line: Predicting the performance of neural networks under dis- tribution shift,

C. Baek, Y . Jiang, A. Raghunathan, and J. Z. Kolter, “Agreement- on-the-line: Predicting the performance of neural networks under dis- tribution shift,” Advances in Neural Information Processing Systems , 2022

2022
[24]

Performance prediction for semantic segmentation by a self-supervised image reconstruction decoder,

A. B ¨ar, M. Klingner, J. L ¨ohdefink, F. H ¨uger, P. Schlicht, and T. Fin- gscheidt, “Performance prediction for semantic segmentation by a self-supervised image reconstruction decoder,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022

2022
[25]

Improving online performance prediction for semantic segmentation,

M. Klingner, A. Bar, M. Mross, and T. Fingscheidt, “Improving online performance prediction for semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni- tion, 2021

2021
[26]

Deep sets,

M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdi- nov, and A. J. Smola, “Deep sets,” Advances in neural information processing systems, vol. 30, 2017

2017
[27]

Learning transferable visual models from natural language supervision,

A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al., “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PmLR, 2021, pp. 8748–8763

2021
[28]

Imagenet: A large-scale hierarchical image database,

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255

2009
[29]

Persformer: 3d lane detection via perspective transformer and the openlane benchmark,

L. Chen, C. Sima, Y . Li, Z. Zheng, J. Xu, X. Geng, H. Li, C. He, J. Shi, Y . Qiao et al. , “Persformer: 3d lane detection via perspective transformer and the openlane benchmark,” in European Conference on Computer Vision. Springer, 2022, pp. 550–567

2022
[30]

An image is worth 16x16 words: Transformers for image recognition at scale,

A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929 , 2020

2010 arXiv
[31]

Dinov2: Learning robust visual features without supervision,

M. Oquab, T. Darcet, T. Moutakanni, H. V o, M. Szafraniec, V . Khali- dov, P. Fernandez, D. Haziza, F. Massa, A. El-Nouby et al., “Dinov2: Learning robust visual features without supervision,” arXiv preprint arXiv:2304.07193, 2023

2023 arXiv

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

Lanenet: Real-time lane detection networks for autonomous driving,

Z. Wang, W. Ren, and Q. Qiu, “Lanenet: Real-time lane detection networks for autonomous driving,” arXiv preprint arXiv:1807.01726 , 2018

arXiv 2018

[2] [2]

Spatial as deep: Spatial cnn for traffic scene understanding,

X. Pan, J. Shi, P. Luo, X. Wang, and X. Tang, “Spatial as deep: Spatial cnn for traffic scene understanding,” in Proceedings of the AAAI conference on artificial intelligence , 2018

work page 2018

[3] [3]

Line-cnn: End-to-end traffic line detection with line proposal unit,

X. Li, J. Li, X. Hu, and J. Yang, “Line-cnn: End-to-end traffic line detection with line proposal unit,” IEEE Transactions on Intelligent Transportation Systems, 2019

work page 2019

[4] [4]

Keep your eyes on the lane: Real-time attention- guided lane detection,

L. Tabelini, R. Berriel, T. M. Paixao, C. Badue, A. F. De Souza, and T. Oliveira-Santos, “Keep your eyes on the lane: Real-time attention- guided lane detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021

work page 2021

[5] [5]

Polylanenet: Lane estimation via deep polynomial regression,

——, “Polylanenet: Lane estimation via deep polynomial regression,” in 2020 25th International Conference on Pattern Recognition (ICPR), 2021

work page 2020

[6] [6]

Clrnet: Cross layer refinement network for lane detection,

T. Zheng, Y . Huang, Y . Liu, W. Tang, Z. Yang, D. Cai, and X. He, “Clrnet: Cross layer refinement network for lane detection,” in Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022

work page 2022

[7] [7]

Qui ˜nonero-Candela, M

J. Qui ˜nonero-Candela, M. Sugiyama, A. Schwaighofer, and N. D. Lawrence, Dataset shift in machine learning . Mit Press, 2009

work page 2009

[8] [8]

Are labels always necessary for classifier accuracy evaluation?

W. Deng and L. Zheng, “Are labels always necessary for classifier accuracy evaluation?” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021

work page 2021