PolyLaneNet: Lane Estimation via Deep Polynomial Regression
- URL: http://arxiv.org/abs/2004.10924v2
- Date: Tue, 14 Jul 2020 17:02:54 GMT
- Title: PolyLaneNet: Lane Estimation via Deep Polynomial Regression
- Authors: Lucas Tabelini, Rodrigo Berriel, Thiago M. Paix\~ao, Claudine Badue,
Alberto F. De Souza and Thiago Oliveira-Santos
- Abstract summary: We present a novel method for lane detection that uses an image from a forward-looking camera mounted in the vehicle.
The proposed method is shown to be competitive with existing state-of-the-art methods in the TuSimple dataset.
We provide source code and trained models that allow others to replicate all the results shown in this paper.
- Score: 9.574421369309949
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: One of the main factors that contributed to the large advances in autonomous
driving is the advent of deep learning. For safer self-driving vehicles, one of
the problems that has yet to be solved completely is lane detection. Since
methods for this task have to work in real-time (+30 FPS), they not only have
to be effective (i.e., have high accuracy) but they also have to be efficient
(i.e., fast). In this work, we present a novel method for lane detection that
uses as input an image from a forward-looking camera mounted in the vehicle and
outputs polynomials representing each lane marking in the image, via deep
polynomial regression. The proposed method is shown to be competitive with
existing state-of-the-art methods in the TuSimple dataset while maintaining its
efficiency (115 FPS). Additionally, extensive qualitative results on two
additional public datasets are presented, alongside with limitations in the
evaluation metrics used by recent works for lane detection. Finally, we provide
source code and trained models that allow others to replicate all the results
shown in this paper, which is surprisingly rare in state-of-the-art lane
detection methods. The full source code and pretrained models are available at
https://github.com/lucastabelini/PolyLaneNet.
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