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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 PSDL, is proposed. Using PSDL, given a surround-view image, the marking-points will be detected first and then the valid parking-slots can be inferred. The efficacy and efficiency of PSDL have been corroborated on our database. It is expected that PSDL can serve as a baseline when the other researchers develop more sophisticated methods.
Lin Zhang; Xiyuan Li; Junhao Huang; Ying Shen; Dongqing Wang. Vision-Based Parking-Slot Detection: A Benchmark and A Learning-Based Approach. Symmetry 2018, 10, 64 .
AMA StyleLin Zhang, Xiyuan Li, Junhao Huang, Ying Shen, Dongqing Wang. Vision-Based Parking-Slot Detection: A Benchmark and A Learning-Based Approach. Symmetry. 2018; 10 (3):64.
Chicago/Turabian StyleLin Zhang; Xiyuan Li; Junhao Huang; Ying Shen; Dongqing Wang. 2018. "Vision-Based Parking-Slot Detection: A Benchmark and A Learning-Based Approach." Symmetry 10, no. 3: 64.