

Author: Zhang Lin Li Xiyuan Huang Junhao Shen Ying Wang Dongqing
Publisher: MDPI
E-ISSN: 2073-8994|10|3|64-64
ISSN: 2073-8994
Source: Symmetry, Vol.10, Iss.3, 2018-03, pp. : 64-64
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Abstract
Recent years have witnessed a growing interest in developing automatic parking systems in the field of intelligent vehicles. However, how to effectively and efficiently locating parking-slots using a vision-based system is still an unresolved issue. Even more seriously, there is no publicly available labeled benchmark dataset for tuning and testing parking-slot detection algorithms. In this paper, we attempt to fill the above-mentioned research gaps to some extent and our contributions are twofold. Firstly, to facilitate the study of vision-based parking-slot detection, a large-scale parking-slot image database is established. This database comprises 8600 surround-view images collected from typical indoor and outdoor parking sites. For each image in this database, the marking-points and parking-slots are carefully labeled. Such a database can serve as a benchmark to design and validate parking-slot detection algorithms. Secondly, a learning-based parking-slot detection approach, namely
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