{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T22:32:09Z","timestamp":1773009129650,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,9,3]],"date-time":"2022-09-03T00:00:00Z","timestamp":1662163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Key Laboratory of Automotive Safety and Energy Conservation, Tsinghua University","award":["KFY2207"],"award-info":[{"award-number":["KFY2207"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The automatic parking system based on vision is greatly affected by uneven lighting, which is difficult to make an accurate judgment on parking spaces in the case of complex image information. To solve this problem, this paper proposes a parking space visual detection and image processing method based on deep learning. Firstly, a 360-degree panoramic system was designed to photograph the vehicle environment. The image has been processed to obtain a panoramic aerial view, which was input as the original image of the parking space detection system. Secondly, the Faster R-CNN (Region-Convolutional Neural Network) parking detection model was established based on deep learning. It was aimed to detect and extract the parking space from the input image. Thirdly, the problems of uneven illumination and complex background were solved effectively by removing the background light from the image. Finally, a parking space extraction method based on the connected region has been designed, which further simplified the parking space extraction and image processing. The experiment results show that the mAP (mean Average Precision) value of the Faster R-CNN model using 101-Floor ResNet as the feature extraction network is 89.30%, which is 2.28% higher than that of the Faster R-CNN model using 50-Floor ResNet as the feature extraction network. The model built in this paper can detect most parking spaces well. The position of the output target box is accurate. In some test scenarios, the confidence of parking space recognition can even reach 100%. In summary, the proposed method can realize the effective identification and accurate positioning of parking spaces.<\/jats:p>","DOI":"10.3390\/s22176672","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"6672","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Visual Detection and Image Processing of Parking Space Based on Deep Learning"],"prefix":"10.3390","volume":"22","author":[{"given":"Chen","family":"Huang","sequence":"first","affiliation":[{"name":"Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China"},{"name":"State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China"}]},{"given":"Shiyue","family":"Yang","sequence":"additional","affiliation":[{"name":"Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China"}]},{"given":"Yugong","family":"Luo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China"}]},{"given":"Yongsheng","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China"}]},{"given":"Ze","family":"Liu","sequence":"additional","affiliation":[{"name":"Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,3]]},"reference":[{"key":"ref_1","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_2","first-page":"8887","article-title":"Design and Development of Low Cost Automatic Parking Assistance System","volume":"975","author":"Reddy","year":"2014","journal-title":"Int. Conf. Inf. Commun. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1007\/s12239-014-0102-y","article-title":"Automatic parking of vehicles: A review of literatures","volume":"15","author":"Wang","year":"2014","journal-title":"Int. J. Automot. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.cviu.2019.03.001","article-title":"A survey of advances in vision-based vehicle re-identification","volume":"182","author":"Khan","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.imavis.2017.07.002","article-title":"Computer vision in automated parking systems: Design, Implementation and challenges","volume":"68","author":"Heimberger","year":"2017","journal-title":"Image Vis. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MC.2014.42","article-title":"Object Detection with Discriminatively Trained Part-Based Models","volume":"47","author":"Forsyth","year":"2014","journal-title":"Computer"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single Shot Multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4570","DOI":"10.1109\/TITS.2020.3046039","article-title":"End-to-End Trainable One-Stage Parking Slot Detection Integrating Global and Local Information","volume":"23","author":"Suhr","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1016\/j.proeng.2017.06.109","article-title":"Visual Localization and Identification of Vehicles Inside a Parking House","volume":"192","author":"Nemec","year":"2017","journal-title":"Procedia Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"61","DOI":"10.4018\/IJWSR.304061","article-title":"Review of Research on Vision-Based Parking Space Detection Method","volume":"19","author":"Ma","year":"2022","journal-title":"Int. J. Web Serv. Res."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yu, Z., Gao, Z., Chen, H., and Huang, Y. (November, January 19). SPFCN: Select and Prune the Fully Convolutional Networks for Real-time Parking Slot Detection. Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA.","DOI":"10.1109\/IV47402.2020.9304688"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sairam, B., Agrawal, A., Krishna, G., and Sahu, S.P. (2020, January 11\u201313). Automated Vehicle Parking Slot Detection System Using Deep Learning. Proceedings of the 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), Erode, India.","DOI":"10.1109\/ICCMC48092.2020.ICCMC-000140"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, L., Lin, L., Liang, X., and He, K. (2016, January 11\u201314). Is Faster R-CNN Doing Well for Pedestrian Detection. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_28"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhang, H., Hasith, K., and Wang, H. (2019, January 3\u20135). Real-time Robust Multi-lane Detection and Tracking in Challenging Urban Scenarios. Proceedings of the IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM), Toyonaka, Japan.","DOI":"10.1109\/ICARM.2019.8834317"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1109\/TITS.2012.2184756","article-title":"A Learning Approach towards Detection and Tracking of Lane Markings. Intelligent Transportation Systems","volume":"13","author":"Gopalan","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Shen, Y., Xiao, T., Li, H., Yi, S., and Wang, X. (2017, January 22\u201329). Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposals. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.210"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, Y., Yan, H., and Liu, J. (2017, January 17\u201320). Deep joint discriminative learning for vehicle re-identification and retrieval. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296310"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8169","DOI":"10.1364\/AO.51.008169","article-title":"Method for calibrating the fisheye distortion center","volume":"51","author":"Huang","year":"2012","journal-title":"Appl. Opt."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Salvador, A., Giro-I-Nieto, X., Marques, F., and Satoh, S. (July, January 26). Faster R-CNN Features for Instance Search. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, NV, USA.","DOI":"10.1109\/CVPRW.2016.56"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, B., Yan, J., Wu, W., Zhu, Z., and Hu, X. (2018, January 18\u201323). High Performance Visual Tracking with Siamese Region Proposal Network. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00935"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, H., and Hou, X. (2012, January 11\u201313). The Precise Location Algorithm of License Plate Based on Gray Image. Proceedings of the International Conference on Computer Science & Service System, Nanjing, China.","DOI":"10.1109\/CSSS.2012.25"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"838","DOI":"10.4304\/jcp.7.4.838-841","article-title":"Application of Improved Median Filter on Image Processing","volume":"7","author":"Zhu","year":"2012","journal-title":"J. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Cheng, Y., and Yue, G. (2021, January 19\u201321). Application of Convolutional Neural Network Technology in Vehicle Parking Management. Proceedings of the CSAE 2021: The 5th International Conference on Computer Science and Application Engineering, Sanya, China.","DOI":"10.1145\/3487075.3487191"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.jpowsour.2014.09.039","article-title":"An experimental study on burning behaviors of 18650 lithium ion batteries using a cone calorimeter","volume":"273","author":"Fu","year":"2015","journal-title":"J. Power Sources"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"10543","DOI":"10.1007\/s11042-018-6583-3","article-title":"2D-to-3D conversion using optical flow based depth generation and cross-scale hole filling algorithm","volume":"78","author":"Yao","year":"2018","journal-title":"Multimedia Tools Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"A2163","DOI":"10.1149\/2.0751510jes","article-title":"Characterization of Lithium-Ion Battery Thermal Abuse Behavior Using Experimental and Computational Analysis","volume":"162","author":"Lopez","year":"2015","journal-title":"J. Electrochem. Soc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.apenergy.2016.04.016","article-title":"Simulation and experimental study on lithium ion battery short circuit","volume":"173","author":"Zhao","year":"2016","journal-title":"Appl. Energy"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.patrec.2017.12.027","article-title":"Hough Transform for real-time plane detection in depth images","volume":"103","author":"Vera","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gonzalez, D., Zimmermann, T., and Nagappan, N. (2020, January 29\u201330). The State of the ML-universe: 10 Years of Artificial Intelligence & Machine Learning Software Development on GitHub. Proceedings of the MSR \u201920: 17th International Conference on Mining Software Repositories, Seoul, Korea.","DOI":"10.1145\/3379597.3387473"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e6006","DOI":"10.1002\/cpe.6006","article-title":"Fully automated roadside parking spot detection in real time with deep learning","volume":"33","year":"2021","journal-title":"Concurrency Computat Pract Exper."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bazzaza, T., Chen, Z., Prabha, S., Tohidypour, H.R., Wang, Y., Pourazad, M.T., Nasiopoulos, P., and Leung, V.C. (2022, January 7\u20139). Automatic Street Parking Space Detection Using Visual Information and Convolutional Neural Networks. Proceedings of the 2022 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE53296.2022.9730584"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6672\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:22:52Z","timestamp":1760142172000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6672"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,3]]},"references-count":34,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22176672"],"URL":"https:\/\/doi.org\/10.3390\/s22176672","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,3]]}}}