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
Recursive Video Lane Detection
dongkwonjin/rvld
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ICCV 2023
A novel algorithm to detect road lanes in videos, called recursive video lane detector (RVLD), is proposed in this paper, which propagates the state of a current frame recursively to the next frame.
ADNet: Lane Shape Prediction via Anchor Decomposition
sephirex-x/adnet
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ICCV 2023
In this paper, we revisit the limitations of anchor-based lane detection methods, which have predominantly focused on fixed anchors that stem from the edges of the image, disregarding their versatility and quality.
Contrastive Learning for Lane Detection via Cross-Similarity
CLLD is a novel multitask contrastive learning that trains lane detection approaches to detect lane markings even in low visible situations by integrating local feature contrastive learning (CL) with our new proposed operation cross-similarity.
LATR: 3D Lane Detection from Monocular Images with Transformer
jmoonr/latr
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ICCV 2023
On the one hand, each query is generated based on 2D lane-aware features and adopts a hybrid embedding to enhance lane information.
TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars
Driveable Area Segmentation and Lane Detection are particularly important for safe and efficient navigation on the road.
LVLane: Deep Learning for Lane Detection and Classification in Challenging Conditions
zillur-av/LVLane
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Experimental evaluations conducted on the widely-used TuSimple dataset, Caltech Lane dataset, and our LVLane dataset demonstrate the effectiveness of our model in accurately detecting and classifying lanes amidst challenging scenarios.
CLRerNet: Improving Confidence of Lane Detection with LaneIoU
Lane marker detection is a crucial component of the autonomous driving and driver assistance systems.
End-to-End Lane detection with One-to-Several Transformer
zkyseu/PPlanedet
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We first propose the one-to-several label assignment, which combines one-to-many and one-to-one label assignment to solve label semantic conflicts while keeping end-to-end detection.
Dense Hybrid Proposal Modulation for Lane Detection
wuyuej/dhpm
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In addition to the shape and location constraints, we design a quality-aware classification loss to adaptively supervise each positive proposal so that the discriminative power can be further boosted.
Learning to Predict Navigational Patterns from Partial Observations
We demonstrate how to infer global navigational patterns by fitting a maximum likelihood graph to the DSLP field.
