https://github.com/CAIC-AD/YOLOPv2
공식 코드YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception
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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.
YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception
OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping
PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark
WS-3D-Lane: Weakly Supervised 3D Lane Detection With 2D Lane Labels
VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
LaneTCA: Enhancing Video Lane Detection with Temporal Context Aggregation
CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution
Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving
Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Evaluating Low-Resource Lane Following Algorithms for Compute-Constrained Automated Vehicles
Inter-Region Affinity Distillation for Road Marking Segmentation
Learning Lightweight Lane Detection CNNs by Self Attention Distillation
Ultra Fast Deep Lane Detection with Hybrid Anchor Driven Ordinal Classification
TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation
RONELD: Robust Neural Network Output Enhancement for Active Lane Detection
Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes
OMR: Occlusion-Aware Memory-Based Refinement for Video Lane Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.