Kavli Affiliate: Feng Yuan
| First 5 Authors: Yaxin Feng, Yuan Lan, Luchan Zhang, Yang Xiang,
| Summary:
The task of lane detection involves identifying the boundaries of driving
areas. Recognizing lanes with complex and variable geometric structures remains
a challenge. In this paper, we introduce a new lane detection framework named
ElasticLaneNet (Elastic-interaction-energy guided Lane detection Network). A
novel and flexible way of representing lanes, namely, implicit representation
is proposed. The training strategy considers predicted lanes as moving curves
that being attracted to the ground truth guided by an elastic interaction
energy based loss function (EIE loss). An auxiliary feature refinement (AFR)
module is designed to gather information from different layers. The method
performs well in complex lane scenarios, including those with large curvature,
weak geometric features at intersections, complicated cross lanes, Y-shapes
lanes, dense lanes, etc. We apply our approach on three datasets: SDLane,
CULane, and TuSimple. The results demonstrate the exceptional performance of
our method, with the state-of-the-art results on the structure-diversity
dataset SDLane, achieving F1-score of 89.51, Recall rate of 87.50, and
Precision of 91.61.
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