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 implementationsLatest papers
Lane2Seq: Towards Unified Lane Detection via Sequence Generation
Experimental results demonstrate that such a simple sequence generation paradigm not only unifies lane detection but also achieves competitive performance on benchmarks.
Sketch and Refine: Towards Fast and Accurate Lane Detection
At the "Sketch" stage, local directions of keypoints can be easily estimated by fast convolutional layers.
FENet: Focusing Enhanced Network for Lane Detection
Inspired by human driving focus, this research pioneers networks augmented with Focusing Sampling, Partial Field of View Evaluation, Enhanced FPN architecture and Directional IoU Loss - targeted innovations addressing obstacles to precise lane detection for autonomous driving.
Building Lane-Level Maps from Aerial Images
In this paper, we introduce for the first time a large-scale aerial image dataset built for lane detection, with high-quality polyline lane annotations on high-resolution images of around 80 kilometers of road.
Towards Unsupervised Representation Learning: Learning, Evaluating and Transferring Visual Representations
Unsupervised representation learning aims at finding methods that learn representations from data without annotation-based signals.
Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps
We propose a novel framework to integrate SD maps into online map prediction and propose a Transformer-based encoder, SD Map Encoder Representations from transFormers, to leverage priors in SD maps for the lane-topology prediction task.
You Only Look at Once for Real-time and Generic Multi-Task
In this study, we present an adaptive, real-time, and lightweight multi-task model designed to concurrently address object detection, drivable area segmentation, and lane line segmentation tasks.
CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching Process
Lane detection plays a crucial role in autonomous driving by providing vital data to ensure safe navigation.
Advancements in 3D Lane Detection Using LiDAR Point Clouds: From Data Collection to Model Development
Advanced Driver-Assistance Systems (ADAS) have successfully integrated learning-based techniques into vehicle perception and decision-making.
DALNet: A Rail Detection Network Based on Dynamic Anchor Line
In the paper, motivated by the anchor line-based lane detection methods, we propose a rail detection network called DALNet based on dynamic anchor line.