Attentional Graph Neural Network for Parking-Slot Detection

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 letter...

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Bibliographic Details
Published inIEEE robotics and automation letters Vol. 6; no. 2; pp. 3445 - 3450
Main Authors Min, Chen, Xu, Jiaolong, Xiao, Liang, Zhao, Dawei, Nie, Yiming, Dai, Bin
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Aggregates
Deep learning for visual perception
Detectors
Feature extraction
graph neural network
Graph neural networks
Manuals
Marking
Neural networks
Parking
parking-slot detection
Post-production processing
recognition
Shape
Training
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Summary: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 letter, 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 https://github.com/Jiaolong/gcn-parking-slot .
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3064270