Attentional Graph Neural Network for Parking-slot Detection

TL;DR

This paper introduces an attentional graph neural network for parking-slot detection that models marking-points as graph data, enabling end-to-end learning and achieving state-of-the-art accuracy without manual post-processing.

Contribution

It proposes a novel graph neural network approach that explicitly models link information between marking-points for parking-slot detection.

Findings

Achieves state-of-the-art accuracy on benchmark datasets.

Eliminates the need for manual post-processing.

Demonstrates effective aggregation of neighboring marking-point information.

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}.