{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T05:16:36Z","timestamp":1782796596988,"version":"3.54.5"},"reference-count":53,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,4,10]],"date-time":"2020-04-10T00:00:00Z","timestamp":1586476800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51605152"],"award-info":[{"award-number":["51605152"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Open Project","award":["31625010"],"award-info":[{"award-number":["31625010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to the complex visual environment, such as lighting variations, shadows, and limitations of vision, the accuracy of vacant parking slot detection for the park assist system (PAS) with a standalone around view monitor (AVM) needs to be improved. To address this problem, we propose a vacant parking slot detection method based on deep learning, namely VPS-Net. VPS-Net converts the vacant parking slot detection into a two-step problem, including parking slot detection and occupancy classification. In the parking slot detection stage, we propose a parking slot detection method based on YOLOv3, which combines the classification of the parking slot with the localization of marking points so that various parking slots can be directly inferred using geometric cues. In the occupancy classification stage, we design a customized network whose size of convolution kernel and number of layers are adjusted according to the characteristics of the parking slot. Experiments show that VPS-Net can detect various vacant parking slots with a precision rate of 99.63% and a recall rate of 99.31% in the ps2.0 dataset, and has a satisfying generalizability in the PSV dataset. By introducing a multi-object detection network and a classification network, VPS-Net can detect various vacant parking slots robustly.<\/jats:p>","DOI":"10.3390\/s20072138","type":"journal-article","created":{"date-parts":[[2020,4,13]],"date-time":"2020-04-13T10:41:52Z","timestamp":1586774512000},"page":"2138","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Vacant Parking Slot Detection in the Around View Image Based on Deep Learning"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1075-8056","authenticated-orcid":false,"given":"Wei","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Libo","family":"Cao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1783-9529","authenticated-orcid":false,"given":"Lingbo","family":"Yan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaohui","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiexing","family":"Feng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peijie","family":"Zhao","sequence":"additional","affiliation":[{"name":"GAC Parts Corporation Limited, Guangzhou 510630, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,10]]},"reference":[{"key":"ref_1","unstructured":"Gallivan, S. (2011). IBM Global Parking Survey: Drivers Share Worldwide Parking Woes Technical Report. IBM. Available online: https:\/\/www-03.ibm.com\/press\/us\/en\/pressrelease\/35515.wss."},{"key":"ref_2","unstructured":"Matthew, A. (2016). Automated Driving: The Technology and Implications for Insurance. Technical Report, Thatcham Research."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1007\/s12239-010-0050-0","article-title":"Low cost design of parallel parking assist system based on an ultrasonic sensor","volume":"11","author":"Jeong","year":"2010","journal-title":"Int. J. Automot. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1109\/TITS.2008.922980","article-title":"Scanning Laser Radar-Based Target Position Designation for Parking Aid System","volume":"9","author":"Jung","year":"2008","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"ref_5","unstructured":"Ullrich, S., Basel, F., Norman, M., and Gerd, W. (2007, January 13\u201315). Free space determination for parking slots using a 3D PMD sensor. Proceedings of the 2007 IEEE Intelligent Vehicles Symposium (IV), Istanbul, Turkey."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Loeffler, A., Ronczka, J., and Fechner, T. (2015, January 24\u201326). Parking lot measurement with 24 GHz short range automotive radar. Proceedings of the 16th International Radar Symposium (IRS), Dresden, Germany.","DOI":"10.1109\/IRS.2015.7226254"},{"key":"ref_7","unstructured":"Kaempchen, N., Franke, U., and Ott, R. (2002, January 17\u201321). Stereo vision based pose estimation of parking lots using 3D vehicle models. Proceedings of the 2002 IEEE Intelligent Vehicle Symposium (IV), Versailles, France."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lee, S., Hyeon, D., Park, G., Baek, I.-J., Kim, S.-W., and Seo, S.-W. (2016, January 19\u201322). Directional-DBSCAN: Parking-slot detection using a clustering method in around-view monitoring system. Proceedings of the 2016 IEEE Intelligent Vehicle Symposium (IV), Gotenburg, Sweden.","DOI":"10.1109\/IVS.2016.7535409"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, Q., Lin, C., and Zhao, Y. (2018). Geometric Features-Based Parking Slot Detection. Sensors, 18.","DOI":"10.3390\/s18092821"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, L., Li, X., Huang, J., Shen, Y., and Wang, D. (2018). Vision-Based Parking-Slot Detection: A Benchmark and A Learning-Based Approach. Symmetry, 10.","DOI":"10.3390\/sym10030064"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5350","DOI":"10.1109\/TIP.2018.2857407","article-title":"Vision-based Parking-slot Detection: A DCNN-based Approach and A Large-scale Benchmark Dataset","volume":"27","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jung, H.G., Kim, D.S., Yoon, P.J., and Kim, J. (2006, January 17\u201319). Structure Analysis Based Parking Slot Marking Recognition for Semi-automatic Parking System. Proceedings of the Joint IAPR International WorkShops on Statistical Techniques in Pattern Recognition and Structural and Syntactic Pattern Recognition, Hong Kong, China.","DOI":"10.1007\/11815921_42"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jung, H.G., Kim, D.S., Yoon, P.J., and Kim, J. (2008, January 4\u20136). Two-Touch Type Parking Slot Marking Recognition for Target Parking Position Designation. Proceedings of the 2008 IEEE Intelligent Vehicles Symposium (IV), Eindhoven, The Netherlands.","DOI":"10.1109\/IVS.2008.4621265"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1109\/TVT.2009.2034860","article-title":"Uniform User Interface for Semiautomatic Parking Slot Marking Recognition","volume":"59","author":"Jung","year":"2010","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, J., Portilla, J., and Riesgo, T. (2012, January 25\u201328). Smart parking service based on wireless sensor networks. Proceedings of the IECON 2012\u201438th Annual Conference on IEEE Industrial Electronics Society, Montreal, QC, Canada.","DOI":"10.1109\/IECON.2012.6389096"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lee, C., Han, Y., Jeon, S., Seo, D., and Jung, I. (2016, January 7\u201311). Smart parking system for Internet of Things. Proceedings of the 2016 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE.2016.7430607"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Balzano, W., and Vitale, F. (2017, January 27\u201329). DiG-Park: A Smart Parking Availability Searching Method Using V2V\/V2I and DGP-Class Problem. Proceedings of the 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA), Taipei, Taiwan.","DOI":"10.1109\/WAINA.2017.104"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Su, C.-L., Lee, C.-J., Li, M.-S., and Chen, K.-P. (2015, January 2\u20134). 3D AVM system for automotive applications. Proceedings of the 10th International Conference on Information, Communications and Signal Processing (ICICS), Singapore.","DOI":"10.1109\/ICICS.2015.7459915"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Suhr, J.K., and Jung, H.G. (2012, January 16\u201319). Fully-automatic Recognition of Various Parking Slot Markings in Around View Monitor (AVM) Image Sequences. Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, AK, USA.","DOI":"10.1109\/ITSC.2012.6338615"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"037203","DOI":"10.1117\/1.OE.52.3.037203","article-title":"Full-automatic recognition of various parking slot markings using a hierarchical tree structure","volume":"52","author":"Suhr","year":"2013","journal-title":"Opt. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TITS.2013.2272100","article-title":"Sensor Fusion-Based Vacant Parking Slot Detection and Tracking","volume":"15","author":"Suhr","year":"2014","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Suhr, J.K., and Jung, H.G. (2018). A Universal Vacant Parking Slot Recognition System Using Sensors Mounted on Off-the-Shelf Vehicles. Sensors, 18.","DOI":"10.3390\/s18041213"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zinelli, A., Musto, L., and Pizzati, F. (, January 9\u201312). A Deep-Learning Approach for Parking Slot Detection on Surround-View Images. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8813777"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wu, Y., Yang, T., Zhao, J., Guan, L., and Jiang, W. (2018, January 26\u201330). VH-HFCN based Parking Slot and Lane Markings Segmentation on Panoramic Surround View. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500553"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Jiang, W., Wu, Y., Guan, L., and Zhao, J. (2019, January 20\u201324). DFNet: Semantic Segmentation on Panoramic Images with Dynamic Loss Weights and Residual Fusion Block. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8794476"},{"key":"ref_26","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hamada, K., Hu, Z., Fan, M., and Chen, H. (July, January 28). Surround view based parking lot detection and tracking. Proceedings of the 2015 IEEE Intelligent Vehicles Symposium (IV), Seoul, Korea.","DOI":"10.1109\/IVS.2015.7225832"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"847406","DOI":"10.1155\/2014\/847406","article-title":"Automatic Parking Based on a Bird\u2019s Eye View Vision System","volume":"6","author":"Wang","year":"2014","journal-title":"Adv. Mech. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s00138-018-0986-z","article-title":"Semantic segmentation-based parking space detection with standalone around view monitoring system","volume":"30","author":"Jang","year":"2019","journal-title":"Mach. Vis. Appl."},{"key":"ref_30","unstructured":"Harris, C.J., and Stephens, M. (September, January 31). A Combined Corner and Edge Detector. In Proceeding of the 4th Alvey Vision Conference, Manchester, UK."},{"key":"ref_31","first-page":"119","article-title":"A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting","volume":"55","author":"Yoav","year":"1999","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5687","DOI":"10.1109\/TIE.2016.2558480","article-title":"Automatic Parking Space Detection and Tracking for Underground and Indoor Environments","volume":"63","author":"Suhr","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, L., Li, C., Zhang, Q., Guo, T., and Miao, Z. (2017, January 11\u201313). Automatic Parking Slot Detection Based on Around View Monitor (AVM) Systems. Proceedings of the 9th International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, China.","DOI":"10.1109\/WCSP.2017.8170903"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1049\/iet-its.2015.0226","article-title":"Available Parking Slot Recognition based on Slot Context Analysis","volume":"10","author":"Lee","year":"2016","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_36","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of Oriented Gradients for Human Detection. Proceedings of the 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA."},{"key":"ref_37","first-page":"435","article-title":"Data classification using Support vector Machine (SVM), a simplified approach","volume":"3","author":"Amarappa","year":"2010","journal-title":"Int. J. Electron. Comput. Sci. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Rianto, D., Erwin, I., Prakasa, E., and Herlan, H. (2018, January 1\u20132). Parking Slot Identification using Local Binary Pattern and Support Vector Machine. Proceedings of the 2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA), Tangerang, Indonesia.","DOI":"10.1109\/IC3INA.2018.8629530"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","article-title":"Multiresolution gray-scale and rotation invariant texture classification with local binary patterns","volume":"24","author":"Ojala","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.eswa.2016.10.055","article-title":"Deep Learning for Decentralized Parking Lot Occupancy Detection","volume":"72","author":"Amato","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Nurullayev, S., and Lee, S.-W. (2019). Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network. Sensors, 19.","DOI":"10.3390\/s19020277"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Paidi, V., and Fleyeh, H. (2019, January 3\u20135). Parking Occupancy Detection Using Thermal Camera. Proceedings of the Vehicle Technology and Intelligent Transport Systems (VEHITS 2019), Heraklion, Greece.","DOI":"10.5220\/0007726800002179"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2015). SSD: Single Shot MultiBox Detector. arXiv.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_48","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_49","unstructured":"Alex, K., Ilya, S., and Hinton, G.E. (2012, January 3\u20138). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Advances in Neural Information Processing Systems 25 (NIPS 2012), Lake Tahoe, NV, USA."},{"key":"ref_50","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"He, K.M., Zhang, X.Y., Ren, S.Q., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (2019). Searching for MobileNetV3. arXiv.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_53","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017, January 23\u201328). Automatic differentiation in PyTorch. Proceedings of the NIPS 2017 Workshop, Long Beach, CA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/7\/2138\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:17:21Z","timestamp":1760174241000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/7\/2138"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,10]]},"references-count":53,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["s20072138"],"URL":"https:\/\/doi.org\/10.3390\/s20072138","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,10]]}}}