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 )

Greatest papers with code

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

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

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 +1

End to End Learning for Self-Driving Cars

marsauto/europilot 25 Apr 2016

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

Ultra Fast Structure-aware Deep Lane Detection

cfzd/Ultra-Fast-Lane-Detection 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.

Lane Detection

Learning Lightweight Lane Detection CNNs by Self Attention Distillation

cardwing/Codes-for-Lane-Detection ICCV 2019

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 +1

Spatial As Deep: Spatial CNN for Traffic Scene Understanding

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

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)

Lane Detection Scene Understanding

CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending

huawei-noah/vega 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.

Autonomous Driving Lane Detection

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

wvangansbeke/LaneDetection_End2End 1 Feb 2019

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

End-to-end Lane Shape Prediction with Transformers

liuruijin17/LSTR 9 Nov 2020

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

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

SeokjuLee/VPGNet ICCV 2017

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

Wizaron/instance-segmentation-pytorch 8 Aug 2017

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 +3