Decoupling the Curve Modeling and Pavement Regression for Lane Detection
- URL: http://arxiv.org/abs/2309.10533v1
- Date: Tue, 19 Sep 2023 11:24:14 GMT
- Title: Decoupling the Curve Modeling and Pavement Regression for Lane Detection
- Authors: Wencheng Han, Jianbing Shen
- Abstract summary: curve-based lane representation is a popular approach in many lane detection methods.
We propose a new approach to the lane detection task by decomposing it into two parts: curve modeling and ground height regression.
- Score: 67.22629246312283
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The curve-based lane representation is a popular approach in many lane
detection methods, as it allows for the representation of lanes as a whole
object and maximizes the use of holistic information about the lanes. However,
the curves produced by these methods may not fit well with irregular lines,
which can lead to gaps in performance compared to indirect representations such
as segmentation-based or point-based methods. We have observed that these lanes
are not intended to be irregular, but they appear zigzagged in the perspective
view due to being drawn on uneven pavement. In this paper, we propose a new
approach to the lane detection task by decomposing it into two parts: curve
modeling and ground height regression. Specifically, we use a parameterized
curve to represent lanes in the BEV space to reflect the original distribution
of lanes. For the second part, since ground heights are determined by natural
factors such as road conditions and are less holistic, we regress the ground
heights of key points separately from the curve modeling. Additionally, we have
unified the 2D and 3D lane detection tasks by designing a new framework and a
series of losses to guide the optimization of models with or without 3D lane
labels. Our experiments on 2D lane detection benchmarks (TuSimple and CULane),
as well as the recently proposed 3D lane detection datasets (ONCE-3Dlane and
OpenLane), have shown significant improvements. We will make our
well-documented source code publicly available.
Related papers
- Monocular Lane Detection Based on Deep Learning: A Survey [51.19079381823076]
Lane detection plays an important role in autonomous driving perception systems.
As deep learning algorithms gain popularity, monocular lane detection methods based on deep learning have demonstrated superior performance.
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.
arXiv Detail & Related papers (2024-11-25T12:09:43Z) - Sketch and Refine: Towards Fast and Accurate Lane Detection [69.63287721343907]
Lane detection is a challenging task due to the complexity of real-world scenarios.
Existing approaches, whether proposal-based or keypoint-based, suffer from depicting lanes effectively and efficiently.
We present a "Sketch-and-Refine" paradigm that utilizes the merits of both keypoint-based and proposal-based methods.
Experiments show that our SRLane can run at a fast speed (i.e., 278 FPS) while yielding an F1 score of 78.9%.
arXiv Detail & Related papers (2024-01-26T09:28:14Z) - 3D Lane Detection from Front or Surround-View using Joint-Modeling & Matching [27.588395086563978]
We propose a joint modeling approach that combines Bezier curves and methods.
We also introduce a novel 3D Spatial, representing an exploration of 3D surround-view lane detection research.
This innovative method establishes a new benchmark in front-view 3D lane detection on the Openlane dataset.
arXiv Detail & Related papers (2024-01-16T01:12:24Z) - BSNet: Lane Detection via Draw B-spline Curves Nearby [21.40607319558899]
We revisit the curve-based lane detection methods from the perspectives of the lane representations' globality and locality.
We design a simple yet efficient network BSNet to ensure the acquisition of global and local features.
The proposed methods achieve state-of-the-art performance on the Tusimple, CULane, and LLAMAS datasets.
arXiv Detail & Related papers (2023-01-17T14:25:40Z) - CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries
and Attention [3.330270927081078]
3D lane detection is an integral part of autonomous driving systems.
Previous CNN and Transformer-based methods usually first generate a bird's-eye-view (BEV) feature map from the front view image.
We propose CurveFormer, a single-stage Transformer-based method that directly calculates 3D lane parameters.
arXiv Detail & Related papers (2022-09-16T14:54:57Z) - RCLane: Relay Chain Prediction for Lane Detection [76.62424079494285]
We present a new method for lane detection based on relay chain prediction.
Our strategy allows us to establish new state-of-the-art on four major benchmarks including TuSimple, CULane, CurveLanes and LLAMAS.
arXiv Detail & Related papers (2022-07-19T16:48:39Z) - CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional
Convolution [39.62595444015094]
We propose CondLaneNet, a novel top-to-down lane detection framework.
We also introduce a conditional lane detection strategy based on conditional convolution and row-wise formulation.
Our method achieves state-of-the-art performance on three benchmark datasets.
arXiv Detail & Related papers (2021-05-11T13:10:34Z) - CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive
Point Blending [102.98909328368481]
CurveLane-NAS is a novel lane-sensitive architecture search framework.
It captures both long-ranged coherent and accurate short-range curve information.
It unifies both architecture search and post-processing on curve lane predictions via point blending.
arXiv Detail & Related papers (2020-07-23T17:23:26Z) - Semi-Local 3D Lane Detection and Uncertainty Estimation [6.296104145657063]
Our method is based on a semi-local, BEV, tile representation that breaks down lanes into simple lane segments.
It combines learning a parametric model for the segments along with a deep feature embedding that is then used to cluster segment together into full lanes.
Our method is the first to output a learning based uncertainty estimation for the lane detection task.
arXiv Detail & Related papers (2020-03-11T12:35:24Z) - Road Curb Detection and Localization with Monocular Forward-view Vehicle
Camera [74.45649274085447]
We propose a robust method for estimating road curb 3D parameters using a calibrated monocular camera equipped with a fisheye lens.
Our approach is able to estimate the vehicle to curb distance in real time with mean accuracy of more than 90%.
arXiv Detail & Related papers (2020-02-28T00:24:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.