Lane Detection
33 papers with code • 6 benchmarks • 7 datasets
Lane detection is the task of detecting lanes on a road from a camera.
( Image credit: End-to-end Lane Detection )
Benchmarks
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BDD100K
CULane
TuSimple
comma 2k19
ELAS
LLAMAS
TuSimple Lane
Greatest papers with code
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.
Ranked #8 on
Lane Detection
on TuSimple
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.
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.
Ranked #18 on
Lane Detection
on CULane
Learning Lightweight Lane Detection CNNs by Self Attention Distillation
cardwing/Codes-for-Lane-Detection
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ICCV 2019
Training deep models for lane detection is challenging due to the very subtle and sparse supervisory signals inherent in lane annotations.
Ranked #1 on
Lane Detection
on BDD100K
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
cardwing/Codes-for-Lane-Detection
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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.
Ranked #6 on
Lane Detection
on TuSimple
(using extra training data)
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending
huawei-noah/vega
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ECCV 2020
In this paper, we propose a novel lane-sensitive architecture search framework named CurveLane-NAS to automatically capture both long-ranged coherent and accurate short-range curve information while unifying both architecture search and post-processing on curve lane predictions via point blending.
Ranked #12 on
Lane Detection
on CULane
End-to-end Lane Detection through Differentiable Least-Squares Fitting
wvangansbeke/LaneDetection_End2End
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The problem with such a two-step approach is that the parameters of the network are not optimized for the true task of interest (estimating the lane curvature parameters) but for a proxy task (segmenting the lane markings), resulting in sub-optimal performance.
End-to-end Lane Shape Prediction with Transformers
liuruijin17/LSTR
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To tackle these issues, we propose an end-to-end method that directly outputs parameters of a lane shape model, using a network built with a transformer to learn richer structures and context.
Ranked #13 on
Lane Detection
on TuSimple
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.
Ranked #1 on
Lane Detection
on Caltech Lanes Washington
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.
Ranked #4 on
Multi-Human Parsing
on MHP v1.0