{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:25:22Z","timestamp":1775471122098,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T00:00:00Z","timestamp":1670976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62277041"],"award-info":[{"award-number":["62277041"]}]},{"name":"National Natural Science Foundation of China","award":["2021111002002004"],"award-info":[{"award-number":["2021111002002004"]}]},{"name":"National Natural Science Foundation of China","award":["SJZX 20211006"],"award-info":[{"award-number":["SJZX 20211006"]}]},{"name":"National Natural Science Foundation of China","award":["S202210497009"],"award-info":[{"award-number":["S202210497009"]}]},{"name":"China Unicom Hubei Branch Bilateral Cooperation Research Funds","award":["62277041"],"award-info":[{"award-number":["62277041"]}]},{"name":"China Unicom Hubei Branch Bilateral Cooperation Research Funds","award":["2021111002002004"],"award-info":[{"award-number":["2021111002002004"]}]},{"name":"China Unicom Hubei Branch Bilateral Cooperation Research Funds","award":["SJZX 20211006"],"award-info":[{"award-number":["SJZX 20211006"]}]},{"name":"China Unicom Hubei Branch Bilateral Cooperation Research Funds","award":["S202210497009"],"award-info":[{"award-number":["S202210497009"]}]},{"name":"Hubei Province Safety Production special fund","award":["62277041"],"award-info":[{"award-number":["62277041"]}]},{"name":"Hubei Province Safety Production special fund","award":["2021111002002004"],"award-info":[{"award-number":["2021111002002004"]}]},{"name":"Hubei Province Safety Production special fund","award":["SJZX 20211006"],"award-info":[{"award-number":["SJZX 20211006"]}]},{"name":"Hubei Province Safety Production special fund","award":["S202210497009"],"award-info":[{"award-number":["S202210497009"]}]},{"name":"National innovation and entrepreneurship training program for college students","award":["62277041"],"award-info":[{"award-number":["62277041"]}]},{"name":"National innovation and entrepreneurship training program for college students","award":["2021111002002004"],"award-info":[{"award-number":["2021111002002004"]}]},{"name":"National innovation and entrepreneurship training program for college students","award":["SJZX 20211006"],"award-info":[{"award-number":["SJZX 20211006"]}]},{"name":"National innovation and entrepreneurship training program for college students","award":["S202210497009"],"award-info":[{"award-number":["S202210497009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The parking problem, which is caused by a low parking space utilization ratio, has always plagued drivers. In this work, we proposed an intelligent detection method based on deep learning technology. First, we constructed a TensorFlow deep learning platform for detecting vehicles. Second, the optimal time interval for extracting video stream images was determined in accordance with the judgment time for finding a parking space and the length of time taken by a vehicle from arrival to departure. Finally, the parking space order and number were obtained in accordance with the data layering method and the TimSort algorithm, and parking space vacancy was judged via the indirect Monte Carlo method. To improve the detection accuracy between vehicles and parking spaces, the distance between the vehicles in the training dataset was greater than that of the vehicles observed during detection. A case study verified the reliability of the parking space order and number and the judgment of parking space vacancies.<\/jats:p>","DOI":"10.3390\/s22249835","type":"journal-article","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T03:43:49Z","timestamp":1671075829000},"page":"9835","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["EPSDNet: Efficient Campus Parking Space Detection via Convolutional Neural Networks and Vehicle Image Recognition for Intelligent Human\u2013Computer Interactions"],"prefix":"10.3390","volume":"22","author":[{"given":"Qing","family":"An","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haojun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2753-2854","authenticated-orcid":false,"given":"Xijiang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4921","DOI":"10.1109\/JIOT.2019.2893866","article-title":"From Cloud Down to Things: An Overview of Machine Learning in Internet of Things","volume":"6","author":"Samie","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1109\/TITS.2013.2283805","article-title":"Design and Evaluation of Charging Station Scheduling Strategies for Electric Vehicles","volume":"15","author":"Timpner","year":"2014","journal-title":"IEEE Trans. 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