Lane Detection

82 papers with code • 11 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 )

Libraries

Use these libraries to find Lane Detection models and implementations
7 papers
44
6 papers
521

Most implemented papers

CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution

aliyun/conditional-lane-detection ICCV 2021

Modern deep-learning-based lane detection methods are successful in most scenarios but struggling for lane lines with complex topologies.

HybridNets: End-to-End Perception Network

datvuthanh/HybridNets 17 Mar 2022

Based on these optimizations, we have developed an end-to-end perception network to perform multi-tasking, including traffic object detection, drivable area segmentation and lane detection simultaneously, called HybridNets, which achieves better accuracy than prior art.

CLRNet: Cross Layer Refinement Network for Lane Detection

Turoad/clrnet CVPR 2022

In this way, we can exploit more contextual information to detect lanes while leveraging local detailed lane features to improve localization accuracy.

LaneNet: Real-Time Lane Detection Networks for Autonomous Driving

klintan/pytorch-lanenet 4 Jul 2018

Lane detection is to detect lanes on the road and provide the accurate location and shape of each lane.

Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks

qinnzou/Robust-Lane-Detection 6 Mar 2019

Specifically, information of each frame is abstracted by a CNN block, and the CNN features of multiple continuous frames, holding the property of time-series, are then fed into the RNN block for feature learning and lane prediction.

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

fabvio/ld-lsi 2 May 2019

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 and Classification using Cascaded CNNs

fabvio/TuSimple-lane-classes 2 Jul 2019

Lane detection is extremely important for autonomous vehicles.

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.

Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection

lucastabelini/LaneATT CVPR 2021

Modern lane detection methods have achieved remarkable performances in complex real-world scenarios, but many have issues maintaining real-time efficiency, which is important for autonomous vehicles.

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.