Lane Detection
84 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 )
Benchmarks
These leaderboards are used to track progress in Lane Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Lane Detection models and implementations
nuScenes
BDD100K
CULane
TuSimple
OpenLane
CurveLanes
comma 2k19
LLAMAS
K-Lane
Latest papers with no code
Improved Generalizability of CNN Based Lane Detection in Challenging Weather Using Adaptive Preprocessing Parameter Tuning
Ensuring the robustness of lane detection systems is essential for the reliability of autonomous vehicles, particularly in the face of diverse weather conditions.
PLCNet: Patch-wise Lane Correction Network for Automatic Lane Correction in High-definition Maps
Vision lane detection with LiDAR position assignment is a prevalent method to acquire initial lanes for HD maps.
3D Lane Detection from Front or Surround-View using Joint-Modeling & Matching
Therefore, accurate lane modeling is essential to align prediction results closely with the environment.
RainSD: Rain Style Diversification Module for Image Synthesis Enhancement using Feature-Level Style Distribution
Finally, we discuss the limitation and the future directions of the deep neural network-based perception algorithms and autonomous driving dataset generation based on image-to-image translation.
ElasticLaneNet: An Efficient Geometry-Flexible Approach for Lane Detection
The task of lane detection involves identifying the boundaries of driving areas in real-time.
Improving Lane Detection Generalization: A Novel Framework using HD Maps for Boosting Diversity
Lane detection is a vital task for vehicles to navigate and localize their position on the road.
Applications of Computer Vision in Autonomous Vehicles: Methods, Challenges and Future Directions
Autonomous vehicle refers to a vehicle capable of perceiving its surrounding environment and driving with little or no human driver input.
[Re] CLRNet: Cross Layer Refinement Network for Lane Detection
The following work is a reproducibility report for CLRNet: Cross Layer Refinement Network for Lane Detection.
Elastic Interaction Energy-Informed Real-Time Traffic Scene Perception
Urban segmentation and lane detection are two important tasks for traffic scene perception.
Decoupling the Curve Modeling and Pavement Regression for Lane Detection
The curve-based lane representation is a popular approach in many lane detection methods, as it allows for the representation of lanes as a whole object and maximizes the use of holistic information about the lanes.
