EPSDNet: Efficient Campus Parking Space Detection via Convolutional Neural Networks and Vehicle Image Recognition for Intelligent Human–Computer Interactions
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 optima...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/24/9835 |
| _version_ | 1797455325251502080 |
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| author | Qing An Haojun Wang Xijiang Chen |
| author_facet | Qing An Haojun Wang Xijiang Chen |
| author_sort | Qing An |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T15:52:48Z |
| format | Article |
| id | doaj.art-ab3400b04dc9498da52516a5f02ece92 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T15:52:48Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ab3400b04dc9498da52516a5f02ece922023-11-24T17:56:05ZengMDPI AGSensors1424-82202022-12-012224983510.3390/s22249835EPSDNet: Efficient Campus Parking Space Detection via Convolutional Neural Networks and Vehicle Image Recognition for Intelligent Human–Computer InteractionsQing An0Haojun Wang1Xijiang Chen2School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, ChinaSchool of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, ChinaThe 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.https://www.mdpi.com/1424-8220/22/24/9835deep learningvehicle detectionparking space detectionconvolutional neural networks |
| spellingShingle | Qing An Haojun Wang Xijiang Chen EPSDNet: Efficient Campus Parking Space Detection via Convolutional Neural Networks and Vehicle Image Recognition for Intelligent Human–Computer Interactions Sensors deep learning vehicle detection parking space detection convolutional neural networks |
| title | EPSDNet: Efficient Campus Parking Space Detection via Convolutional Neural Networks and Vehicle Image Recognition for Intelligent Human–Computer Interactions |
| title_full | EPSDNet: Efficient Campus Parking Space Detection via Convolutional Neural Networks and Vehicle Image Recognition for Intelligent Human–Computer Interactions |
| title_fullStr | EPSDNet: Efficient Campus Parking Space Detection via Convolutional Neural Networks and Vehicle Image Recognition for Intelligent Human–Computer Interactions |
| title_full_unstemmed | EPSDNet: Efficient Campus Parking Space Detection via Convolutional Neural Networks and Vehicle Image Recognition for Intelligent Human–Computer Interactions |
| title_short | EPSDNet: Efficient Campus Parking Space Detection via Convolutional Neural Networks and Vehicle Image Recognition for Intelligent Human–Computer Interactions |
| title_sort | epsdnet efficient campus parking space detection via convolutional neural networks and vehicle image recognition for intelligent human computer interactions |
| topic | deep learning vehicle detection parking space detection convolutional neural networks |
| url | https://www.mdpi.com/1424-8220/22/24/9835 |
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