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}.
@article{arxiv.2104.02576,
title = {Attentional Graph Neural Network for Parking-slot Detection},
author = {Chen Min and Jiaolong Xu and Liang Xiao and Dawei Zhao and Yiming Nie and Bin Dai},
journal= {arXiv preprint arXiv:2104.02576},
year = {2021}
}