Attentional Graph Neural Network for Parking-slot Detection

• URL: http://arxiv.org/abs/2104.02576v1
• Date: Tue, 6 Apr 2021 15:14:39 GMT
• Title: Attentional Graph Neural Network for Parking-slot Detection
• Authors: Chen Min and Jiaolong Xu and Liang Xiao and Dawei Zhao and Yiming Nie and Bin Dai
• Abstract summary: 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. Without any manually designed post-processing, the proposed method is end-to-end trainable. Experiments have been conducted on public benchmark dataset, where the proposed method achieves state-of-the-art accuracy.
• Score: 10.095984750382478
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}.

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