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.
Unsupervised Domain Adaptive Lane Detection via Contextual Contrast and Aggregation
RepVF: A Unified Vector Fields Representation for Multi-task 3D Perception
LSM: A Comprehensive Metric for Assessing the Safety of Lane Detection Systems in Autonomous Driving
DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view Representation
Federated Adversarial Learning for Robust Autonomous Landing Runway Detection
LaneCPP: Continuous 3D Lane Detection using Physical Priors
Enhancing 3D Lane Detection and Topology Reasoning with 2D Lane Priors
LanEvil: Benchmarking the Robustness of Lane Detection to Environmental Illusions
TopoLogic: An Interpretable Pipeline for Lane Topology Reasoning on Driving Scenes
CLRKDNet: Speeding up Lane Detection with Knowledge Distillation
Selective Focus: Investigating Semantics Sensitivity in Post-training Quantization for Lane Detection
Towards Robust Physical-world Backdoor Attacks on Lane Detection
Jointly Learning Spatial, Angular, and Temporal Information for Enhanced Lane Detection
Towards Scenario- and Capability-Driven Dataset Development and Evaluation: An Approach in the Context of Mapless Automated Driving
BezierFormer: A Unified Architecture for 2D and 3D Lane Detection
LaneCorrect: Self-supervised Lane Detection
How to deal with glare for improved perception of Autonomous Vehicles
Sparse Laneformer
Monocular 3D lane detection for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
ENet-21: An Optimized light CNN Structure for Lane Detection
TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation
LDTR: Transformer-based Lane Detection with Anchor-chain Representation
SparseFusion: Efficient Sparse Multi-Modal Fusion Framework for Long-Range 3D Perception
A Survey of Vision Transformers in Autonomous Driving: Current Trends and Future Directions
LanePtrNet: Revisiting Lane Detection as Point Voting and Grouping on Curves
Lane2Seq: Towards Unified Lane Detection via Sequence Generation
CurveFormer++: 3D Lane Detection by Curve Propagation with Temporal Curve Queries and Attention
Improved Generalizability of CNN Based Lane Detection in Challenging Weather Using Adaptive Preprocessing Parameter Tuning
Sketch and Refine: Towards Fast and Accurate Lane Detection
PLCNet: Patch-wise Lane Correction Network for Automatic Lane Correction in High-definition Maps
3D Lane Detection from Front or Surround-View using Joint-Modeling & Matching
RainSD: Rain Style Diversification Module for Image Synthesis Enhancement using Feature-Level Style Distribution
FENet: Focusing Enhanced Network for Lane Detection
Building Lane-Level Maps from Aerial Images
ElasticLaneNet: An Efficient Geometry-Flexible Approach for Lane Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.