Lane Detection
51 papers with code • 8 benchmarks • 13 datasets
Lane detection is the task of detecting lanes on a road from a camera.
( 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
BDD100K
TuSimple
CULane
comma 2k19
CurveLanes
LLAMAS
ELAS
DET
OpenLane
TuSimple Lane
Most implemented papers
End to End Learning for Self-Driving Cars
The system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal.
Towards End-to-End Lane Detection: an Instance Segmentation Approach
MaybeShewill-CV/lanenet-lane-detection
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By doing so, we ensure a lane fitting which is robust against road plane changes, unlike existing approaches that rely on a fixed, pre-defined transformation.
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
koyeongmin/PINet
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In the case of traffic line detection, an essential perception module, many condition should be considered, such as number of traffic lines and computing power of the target system.
Semantic Instance Segmentation with a Discriminative Loss Function
Wizaron/instance-segmentation-pytorch
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In this work we propose to tackle the problem with a discriminative loss function, operating at the pixel level, that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step.
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
XingangPan/SCNN
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Although CNN has shown strong capability to extract semantics from raw pixels, its capacity to capture spatial relationships of pixels across rows and columns of an image is not fully explored.
Ultra Fast Structure-aware Deep Lane Detection
cfzd/Ultra-Fast-Lane-Detection
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ECCV 2020
Modern methods mainly regard lane detection as a problem of pixel-wise segmentation, which is struggling to address the problem of challenging scenarios and speed.
YOLOP: You Only Look Once for Panoptic Driving Perception
hustvl/yolop
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A panoptic driving perception system is an essential part of autonomous driving.
VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions.
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving.
LaneNet: Real-Time Lane Detection Networks for Autonomous Driving
Lane detection is to detect lanes on the road and provide the accurate location and shape of each lane.
