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

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