Attentional Graph Neural Network for Parking-slot Detection
| Autor: | Min, Chen, Xu, Jiaolong, Xiao, Liang, Zhao, Dawei, Nie, Yiming, Dai, Bin |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | IEEE Robotics and Automation Letters, vol.6, pp. 3445-3450, 2021 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/LRA.2021.3064270 |
| Popis: | 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}. Comment: Accepted by RAL |
| Databáze: | arXiv |
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