https://github.com/fabvio/ld-lsi
공식 코드Enhanced free space detection in multiple lanes based on single CNN with scene identification
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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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Enhanced free space detection in multiple lanes based on single CNN with scene identification
Recurrent Generic Contour-based Instance Segmentation with Progressive Learning
TopoPoint: Enhance Topology Reasoning via Endpoint Detection in Autonomous Driving
Model-Agnostic Defense for Lane Detection against Adversarial Attack
CLRerNet: Improving Confidence of Lane Detection with LaneIoU
Enhancing 3D Lane Detection and Topology Reasoning with 2D Lane Priors
MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction
RepVF: A Unified Vector Fields Representation for Multi-task 3D Perception
ScalableMap: Scalable Map Learning for Online Long-Range Vectorized HD Map Construction
DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view Representation
LATR: 3D Lane Detection from Monocular Images with Transformer
K-Lane: Lidar Lane Dataset and Benchmark for Urban Roads and Highways
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints
Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection
PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images
Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models
Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps
An intelligent modular real-time vision-based system for environment perception
Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection
Learning to Predict Navigational Patterns from Partial Observations
Rail Detection: An Efficient Row-based Network and A New Benchmark
CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching Process
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.