{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T22:30:54Z","timestamp":1773009054886,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T00:00:00Z","timestamp":1690934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Higher Education (MoHE) under the Fundamental Research Grant Scheme (FRGS)","award":["FRGS\/1\/2022\/ICT08\/MMU\/02\/1"],"award-info":[{"award-number":["FRGS\/1\/2022\/ICT08\/MMU\/02\/1"]}]},{"name":"Ministry of Higher Education (MoHE) under the Fundamental Research Grant Scheme (FRGS)","award":["MMUE\/220041"],"award-info":[{"award-number":["MMUE\/220041"]}]},{"name":"Ministry of Higher Education (MoHE) under the Fundamental Research Grant Scheme (FRGS)","award":["MMUI\/220041"],"award-info":[{"award-number":["MMUI\/220041"]}]},{"name":"Multimedia University (MMU)","award":["FRGS\/1\/2022\/ICT08\/MMU\/02\/1"],"award-info":[{"award-number":["FRGS\/1\/2022\/ICT08\/MMU\/02\/1"]}]},{"name":"Multimedia University (MMU)","award":["MMUE\/220041"],"award-info":[{"award-number":["MMUE\/220041"]}]},{"name":"Multimedia University (MMU)","award":["MMUI\/220041"],"award-info":[{"award-number":["MMUI\/220041"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Autonomous vehicles are gaining popularity, and the development of automatic parking systems is a fundamental requirement. Detecting the parking slots accurately is the first step towards achieving an automatic parking system. However, modern parking slots present various challenges for detection task due to their different shapes, colors, functionalities, and the influence of factors like lighting and obstacles. In this comprehensive review paper, we explore the realm of vision-based deep learning methods for parking slot detection. We categorize these methods into four main categories: object detection, image segmentation, regression, and graph neural network, and provide detailed explanations and insights into the unique features and strengths of each category. Additionally, we analyze the performance of these methods using three widely used datasets: the Tongji Parking-slot Dataset 2.0 (ps 2.0), Sejong National University (SNU) dataset, and panoramic surround view (PSV) dataset, which have played a crucial role in assessing advancements in parking slot detection. Finally, we summarize the findings of each method and outline future research directions in this field.<\/jats:p>","DOI":"10.3390\/s23156869","type":"journal-article","created":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T11:17:17Z","timestamp":1690975037000},"page":"6869","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Review of Vision-Based Deep Learning Parking Slot Detection on Surround View Images"],"prefix":"10.3390","volume":"23","author":[{"given":"Guan Sheng","family":"Wong","sequence":"first","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}]},{"given":"Kah Ong Michael","family":"Goh","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0901-3831","authenticated-orcid":false,"given":"Connie","family":"Tee","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4758-5400","authenticated-orcid":false,"given":"Aznul Qalid","family":"Md. Sabri","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104703","DOI":"10.1016\/j.infrared.2023.104703","article-title":"YOLO-CIR: The Network Based on YOLO and ConvNeXt for Infrared Object Detection","volume":"131","author":"Zhou","year":"2023","journal-title":"Infrared Phys. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104670","DOI":"10.1016\/j.infrared.2023.104670","article-title":"Spatial Infrared Objects Discrimination Based on Multi-Channel CNN with Attention Mechanism","volume":"132","author":"Zhang","year":"2023","journal-title":"Infrared Phys. 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