Lane Detection
84 papers with code • 11 benchmarks • 15 datasets
Lane Detection is a computer vision task that involves identifying the boundaries of driving lanes in a video or image of a road scene. The goal is to accurately locate and track the lane markings in real-time, even in challenging conditions such as poor lighting, glare, or complex road layouts.
Lane detection is an important component of advanced driver assistance systems (ADAS) and autonomous vehicles, as it provides information about the road layout and the position of the vehicle within the lane, which is crucial for navigation and safety. The algorithms typically use a combination of computer vision techniques, such as edge detection, color filtering, and Hough transforms, to identify and track the lane markings in a road scene.
( Image credit: End-to-end Lane Detection )
Benchmarks
These leaderboards are used to track progress in Lane Detection
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Libraries
Use these libraries to find Lane Detection models and implementations
nuScenes
BDD100K
CULane
TuSimple
OpenLane
CurveLanes
comma 2k19
LLAMAS
ELAS
Latest papers
OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping
OpenDriveLab/OpenLane-V2
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NeurIPS 2023
Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments.
Rail Detection: An Efficient Row-based Network and A New Benchmark
sampson-lee/rail-detection
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Inspired by the growth of lane detection, we propose a rail database and a row-based rail detection method.
An intelligent modular real-time vision-based system for environment perception
pandas-team/autonomous-vehicle-environment-perception
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Each section is accompanied by novel techniques to improve the accuracy of others along with the entire system.
Recurrent Generic Contour-based Instance Segmentation with Progressive Learning
It maintains a single estimate of the contour that is progressively deformed toward the object boundary.
Anchor3DLane: Learning to Regress 3D Anchors for Monocular 3D Lane Detection
tusen-ai/anchor3dlane
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CVPR 2023
An attempt has been made to get rid of BEV and predict 3D lanes from FV representations directly, while it still underperforms other BEV-based methods given its lack of structured representation for 3D lanes.
Generating Dynamic Kernels via Transformers for Lane Detection
czyczyyzc/CondLSTR
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ICCV 2023
While such methods reduce the reliance on specific knowledge, the kernels computed from the key locations fail to capture the lane line's global structure due to its long and thin structure, leading to inaccurate detection of lane lines with complex topologies.
Row-wise LiDAR Lane Detection Network with Lane Correlation Refinement
In addition, the second-stage network is found to be especially robust to lane occlusions, thus, demonstrating the robustness of the proposed network for driving in crowded environments.
WS-3D-Lane: Weakly Supervised 3D Lane Detection With 2D Lane Labels
To the best of our knowledge, WS-3D-Lane is the first try of 3D lane detection under weakly supervised setting.
PriorLane: A Prior Knowledge Enhanced Lane Detection Approach Based on Transformer
Lane detection is one of the fundamental modules in self-driving.
3DLaneNAS: Neural Architecture Search for Accurate and Light-Weight 3D Lane Detection
HERO-MDH/3DLaneNAS
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International Conference on Artificial Neural Networks 2022 2022
Lane detection is one of the most fundamental tasks for autonomous driving.
