About

Lane detection is the task of detecting lanes on a road from a camera.

( Image credit: End-to-end Lane Detection )

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Datasets

Greatest papers with code

Towards End-to-End Lane Detection: an Instance Segmentation Approach

15 Feb 2018MaybeShewill-CV/lanenet-lane-detection

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.

INSTANCE SEGMENTATION LANE DETECTION SEMANTIC SEGMENTATION

End to End Learning for Self-Driving Cars

25 Apr 2016marsauto/europilot

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.

LANE DETECTION SELF-DRIVING CARS

Learning Lightweight Lane Detection CNNs by Self Attention Distillation

ICCV 2019 cardwing/Codes-for-Lane-Detection

Training deep models for lane detection is challenging due to the very subtle and sparse supervisory signals inherent in lane annotations.

KNOWLEDGE DISTILLATION LANE DETECTION REPRESENTATION LEARNING

Spatial As Deep: Spatial CNN for Traffic Scene Understanding

17 Dec 2017cardwing/Codes-for-Lane-Detection

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 #4 on Lane Detection on TuSimple (using extra training data)

LANE DETECTION SCENE UNDERSTANDING

Ultra Fast Structure-aware Deep Lane Detection

ECCV 2020 cfzd/Ultra-Fast-Lane-Detection

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.

LANE DETECTION

End-to-end Lane Detection through Differentiable Least-Squares Fitting

1 Feb 2019wvangansbeke/LaneDetection_End2End

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.

LANE DETECTION

VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition

ICCV 2017 SeokjuLee/VPGNet

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.

LANE DETECTION

Semantic Instance Segmentation with a Discriminative Loss Function

8 Aug 2017Wizaron/instance-segmentation-pytorch

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.

INSTANCE SEGMENTATION LANE DETECTION METRIC LEARNING MULTI-HUMAN PARSING SEMANTIC SEGMENTATION

End-to-end Lane Shape Prediction with Transformers

9 Nov 2020liuruijin17/LSTR

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.

LANE DETECTION

Learning to Cluster for Proposal-Free Instance Segmentation

17 Mar 2018GT-RIPL/L2C

We utilize the most fundamental property of instance labeling -- the pairwise relationship between pixels -- as the supervision to formulate the learning objective, then apply it to train a fully convolutional network (FCN) for learning to perform pixel-wise clustering.

AUTONOMOUS DRIVING INSTANCE SEGMENTATION LANE DETECTION OBJECT DETECTION SEMANTIC SEGMENTATION