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SotA/Computer Vision/Lane Detection

Lane Detection

47 benchmarks251 papers

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>

Benchmarks

Lane Detection on CULane

F1 scoremF1

Lane Detection on TuSimple

F1 scoreAccuracy

Lane Detection on OpenLane

F1 (all)Up & DownCurveExtreme WeatherNightIntersectionMerge & SplitF1 scoreFPS (pytorch)

Lane Detection on CurveLanes

F1 scoreGFLOPsPrecisionRecallFPS

Lane Detection on BDD100K val

IoU (%)Accuracy (%)Params (M)

Lane Detection on Apollo Synthetic 3D Lane

F1X error nearX error farZ error nearZ error far

Lane Detection on LLAMAS

F1mF1

Lane Detection on OpenLane-V2 val

OLSDET_lDET_tTOP_llTOP_ltmAP

Lane Detection on nuScenes

IoUF1 score

Lane Detection on Caltech Lanes Cordova

F1

Lane Detection on Caltech Lanes Washington

F1

Lane Detection on DET

Average IOUevent-based F1 score

Lane Detection on K-Lane

F1

Lane Detection on OpenLane-V2 test

OLSDET_lDET_tTOP_llTOP_lt

Lane Detection on tvtLane

F1