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Lane Detection

Autonomous Vehicles › Lane Detection 201 평가 기록 17 datasets

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>

개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.

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Dataset기록된 모델 예시MetricPaperCode
DLNet
F1 score: 81.23
논문 코드
SCNN_UNet_Attention_PL*
Accuracy: 98.38
논문 -
GLane3D(Swin-B)
F1 (all): 66.0Up & Down: 61.7Curve: 72.7Extreme Weather: 63.8
논문 -
CondLSTR (ResNet-101)
F1 score: 88.47
논문 코드
TwinLiteNetPlus-Large
Accuracy (%): 81.9Params (M): 1.94IoU (%): 34.2
논문 코드
LaneCPP
F1: 97.4X error near: 0.030X error far: 0.277Z error near: 0.011
논문 -
CLRNet (DLA-34)
F1: 0.9612
논문 코드
TopoBDA
DET_l: 51.2OLS: 58.7DET_t: 52.0TOP_ll: 39.8
논문 -
CondLSTR (ResNet-18)
F1 score: 60.1
논문 코드
DSLP
IoU: 0.453F1 score: 0.853
논문 코드

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