Lane Detection

77 papers with code • 10 benchmarks • 15 datasets

Lane Detection is a computer vision task that involves identifying the boundaries of driving lanes in a video or image of a road scene. The goal is to accurately locate and track the lane markings in real-time, even in challenging conditions such as poor lighting, glare, or complex road layouts.

Lane detection is an important component of advanced driver assistance systems (ADAS) and autonomous vehicles, as it provides information about the road layout and the position of the vehicle within the lane, which is crucial for navigation and safety. The algorithms typically use a combination of computer vision techniques, such as edge detection, color filtering, and Hough transforms, to identify and track the lane markings in a road scene.

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


Use these libraries to find Lane Detection models and implementations
6 papers
6 papers

Most implemented papers

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.

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.

Key Points Estimation and Point Instance Segmentation Approach for Lane Detection

koyeongmin/PINet 16 Feb 2020

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 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.

Spatial As Deep: Spatial CNN for Traffic Scene Understanding

XingangPan/SCNN 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.

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.

YOLOP: You Only Look Once for Panoptic Driving Perception

hustvl/yolop 25 Aug 2021

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

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.

BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

bdd100k/bdd100k CVPR 2020

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.

RESA: Recurrent Feature-Shift Aggregator for Lane Detection

ZJULearning/resa 31 Aug 2020

Lane detection is one of the most important tasks in self-driving.