Lane Detection
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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.
<span style="color:grey; opacity: 0.6">( Image credit: End-to-end Lane Detection )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
RelTopo: Enhancing Relational Modeling for Driving Scene Topology Reasoning
Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models
TopoPoint: Enhance Topology Reasoning via Endpoint Detection in Autonomous Driving
Safety2Drive: Safety-Critical Scenario Benchmark for the Evaluation of Autonomous Driving
DB3D-L: Depth-aware BEV Feature Transformation for Accurate 3D Lane Detection
OpenLKA: An Open Dataset of Lane Keeping Assist from Recent Car Models under Real-world Driving Conditions
CleanMAP: Distilling Multimodal LLMs for Confidence-Driven Crowdsourced HD Map Updates
Datasets for Lane Detection in Autonomous Driving: A Comprehensive Review
A Robust Real-Time Lane Detection Method with Fog-Enhanced Feature Fusion for Foggy Conditions
GLane3D : Detecting Lanes with Graph of 3D Keypoints
Robust Lane Detection with Wavelet-Enhanced Context Modeling and Adaptive Sampling
CLRNetV2: A Faster and Stronger Lane Detector
TriLiteNet: Lightweight Model for Multi-Task Visual Perception
Rethinking Lanes and Points in Complex Scenarios for Monocular 3D Lane Detection
Advancing Autonomous Vehicle Intelligence: Deep Learning and Multimodal LLM for Traffic Sign Recognition and Robust Lane Detection
TopoBDA: Towards Bezier Deformable Attention for Road Topology Understanding
Anchor3DLane++: 3D Lane Detection via Sample-Adaptive Sparse 3D Anchor Regression
RowDetr: End-to-End Row Detection Using Polynomials
End-to-End Steering for Autonomous Vehicles via Conditional Imitation Co-Learning
Monocular Lane Detection Based on Deep Learning: A Survey
Implementation of Real-Time Lane Detection on Autonomous Mobile Robot
Attention-based U-Net Method for Autonomous Lane Detection
Polar R-CNN: End-to-End Lane Detection with Fewer Anchors
Inadequate contrast ratio of road markings as an indicator for ADAS failure
Technical Report of 1:10 Scale Autonomous Vehicle Robot
QuadBEV: An Efficient Quadruple-Task Perception Framework via Bird's-Eye-View Representation
Lane Detection System for Driver Assistance in Vehicles
TopoMaskV2: Enhanced Instance-Mask-Based Formulation for the Road Topology Problem
Cross Dataset Analysis and Network Architecture Repair for Autonomous Car Lane Detection
Evaluating Low-Resource Lane Following Algorithms for Compute-Constrained Automated Vehicles
LaneTCA: Enhancing Video Lane Detection with Temporal Context Aggregation
LOID: Lane Occlusion Inpainting and Detection for Enhanced Autonomous Driving Systems
HeightLane: BEV Heightmap guided 3D Lane Detection
OMR: Occlusion-Aware Memory-Based Refinement for Video Lane Detection
Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.