Corpus ID: 227049250

Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection

@article{Tabelini2020KeepYE,
  title={Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection},
  author={Lucas Tabelini and Rodrigo Berriel and T. M. Paix{\~a}o and C. Badue and A. Souza and Thiago Oliveira-Santos},
  journal={arXiv: Computer Vision and Pattern Recognition},
  year={2020}
}
Modern lane detection methods have achieved remarkable performances in complex real-world scenarios, but many have issues maintaining real-time efficiency, which is important for autonomous vehicles. In this work, we propose LaneATT: an anchor-based deep lane detection model, which, akin to other generic deep object detectors, uses the anchors for the feature pooling step. Since lanes follow a regular pattern and are highly correlated, we hypothesize that in some cases global information may be… Expand
1 Citations

Figures and Tables from this paper

Driver Distraction Detection Methods: A Literature Review and Framework
  • PDF

References

SHOWING 1-10 OF 29 REFERENCES
PointLaneNet: Efficient end-to-end CNNs for Accurate Real-Time Lane Detection
  • 11
End-to-End Lane Marker Detection via Row-wise Classification
  • Seungwoo Yoo, Heeseok Lee, +4 authors D. Kim
  • Computer Science
  • 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • 2020
  • 7
  • PDF
Lane detection using lane boundary marker network with road geometry constraints
  • 5
  • PDF
Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks
  • 54
  • PDF
End-to-end Lane Detection through Differentiable Least-Squares Fitting
  • 37
  • PDF
Learning Lightweight Lane Detection CNNs by Self Attention Distillation
  • 89
  • Highly Influential
  • PDF
...
1
2
3
...