https://github.com/cv-team/lane_detection_ML
Towards End-to-End Lane Detection: an Instance Segmentation Approach
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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.
Towards End-to-End Lane Detection: an Instance Segmentation Approach
Semantic Instance Segmentation with a Discriminative Loss Function
Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes
TopoLogic: An Interpretable Pipeline for Lane Topology Reasoning on Driving Scenes
Towards End-to-End Lane Detection: an Instance Segmentation Approach
Semantic Instance Segmentation with a Discriminative Loss Function
Semantic Instance Segmentation with a Discriminative Loss Function
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
Semantic Instance Segmentation with a Discriminative Loss Function
Towards End-to-End Lane Detection: an Instance Segmentation Approach
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.