{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:05:53Z","timestamp":1771466753599,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,3,13]],"date-time":"2018-03-13T00:00:00Z","timestamp":1520899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>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     P S  D L     , is proposed. Using     P S  D L     , 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     P S  D L      have been corroborated on our database. It is expected that     P S  D L      can serve as a baseline when the other researchers develop more sophisticated methods.<\/jats:p>","DOI":"10.3390\/sym10030064","type":"journal-article","created":{"date-parts":[[2018,3,13]],"date-time":"2018-03-13T13:37:21Z","timestamp":1520948241000},"page":"64","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Vision-Based Parking-Slot Detection: A Benchmark and A Learning-Based Approach"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4360-5523","authenticated-orcid":false,"given":"Lin","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiyuan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junhao","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongqing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7131","DOI":"10.1109\/TIE.2014.2321342","article-title":"Development of autonomous car\u2014Part I: Distributed system architecture and development process","volume":"61","author":"Jo","year":"2014","journal-title":"IEEE Trans. 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