Lane Detection

9 papers with code · Computer Vision
Subtask of Autonomous Vehicles

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

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Greatest papers with code

End to End Learning for Self-Driving Cars

25 Apr 2016marshq/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

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

Spatial As Deep: Spatial CNN for Traffic Scene Understanding

17 Dec 2017XingangPan/SCNN

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.

LANE DETECTION SCENE UNDERSTANDING

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

Agnostic Lane Detection

2 May 2019cardwing/Codes-for-Lane-Detection

Lane detection is an important yet challenging task in autonomous driving, which is affected by many factors, e. g., light conditions, occlusions caused by other vehicles, irrelevant markings on the road and the inherent long and thin property of lanes.

AUTONOMOUS DRIVING INSTANCE SEGMENTATION LANE DETECTION MULTI-TASK LEARNING SEMANTIC SEGMENTATION

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

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

Semantic Instance Segmentation with a Discriminative Loss Function

8 Aug 2017alicranck/instance-seg

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

Enhanced free space detection in multiple lanes based on single CNN with scene identification

2 May 2019fabvio/ld-lsi

Traditional algorithms usually estimate only the position of the lanes on the road, but an autonomous control system may also need to know if a lane marking can be crossed or not, and what portion of space inside the lane is free from obstacles, to make safer control decisions.

LANE DETECTION