  Deep learning has recently demonstrated its promising performance for
vision-based parking-slot detection. However, very few existing methods
explicitly take into account learning the link information of the
marking-points, resulting in complex post-processing and erroneous detection.
In this paper, we propose an attentional graph neural network based
parking-slot detection method, which refers the marking-points in an
around-view image as graph-structured data and utilize graph neural network to
aggregate the neighboring information between marking-points. Without any
manually designed post-processing, the proposed method is end-to-end trainable.
Extensive experiments have been conducted on public benchmark dataset, where
the proposed method achieves state-of-the-art accuracy. Code is publicly
available at \url{https://github.com/Jiaolong/gcn-parking-slot}.
