End-to-End Lane Marker Detection via Row-wise Classification
- URL: http://arxiv.org/abs/2005.08630v1
- Date: Wed, 6 May 2020 12:48:46 GMT
- Title: End-to-End Lane Marker Detection via Row-wise Classification
- Authors: Seungwoo Yoo, Heeseok Lee, Heesoo Myeong, Sungrack Yun, Hyoungwoo
Park, Janghoon Cho, Duck Hoon Kim
- Abstract summary: In autonomous driving, detecting reliable and accurate lane marker positions is a crucial yet challenging task.
Conventional approaches for the lane marker detection problem perform a pixel-level dense prediction task followed by sophisticated post-processing.
We propose a method performing direct lane marker prediction in an end-to-end manner, without any post-processing step.
- Score: 13.82948181492772
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In autonomous driving, detecting reliable and accurate lane marker positions
is a crucial yet challenging task. The conventional approaches for the lane
marker detection problem perform a pixel-level dense prediction task followed
by sophisticated post-processing that is inevitable since lane markers are
typically represented by a collection of line segments without thickness. In
this paper, we propose a method performing direct lane marker vertex prediction
in an end-to-end manner, i.e., without any post-processing step that is
required in the pixel-level dense prediction task. Specifically, we translate
the lane marker detection problem into a row-wise classification task, which
takes advantage of the innate shape of lane markers but, surprisingly, has not
been explored well. In order to compactly extract sufficient information about
lane markers which spread from the left to the right in an image, we devise a
novel layer, which is utilized to successively compress horizontal components
so enables an end-to-end lane marker detection system where the final lane
marker positions are simply obtained via argmax operations in testing time.
Experimental results demonstrate the effectiveness of the proposed method,
which is on par or outperforms the state-of-the-art methods on two popular lane
marker detection benchmarks, i.e., TuSimple and CULane.
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