Qiankun Li
ABSTRACT
Lanes play a crucial role in visual navigation systems for Autonomous driving. Several studies have employed deep learning technology to design networks for lane detection. However, most methods simply detect lanes area, ignoring that different types of lanes as traffic signs carry different high-level semantic meanings. In this paper, we propose a plug-and-play multi-class lane detection module (MLDM) aimed at distinguishing different kinds of lanes. The module identifies lane regions based on the coordinates of the lanes as predicted by the network. It then uses pixel density and lane length to detect color and determine whether a lane is solid or dashed. In addition, to demonstrate the practical value of MLDM, we devise a lane departure warning system that integrates the detection results of MLDM. Experimental results demonstrate the efficacy of the proposed method.
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Index Terms
- Plug-and-Play Multi-class Lane Detection Module
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