Cloud-Based License Plate Recognition: A Comparative Approach Using You Only Look Once Versions 5, 7, 8, and 9 Object Detection
Cloud-based license plate recognition (LPR) systems have emerged as essential tools in modern traffic management and security applications. Determining the best approach remains paramount in the field of computer vision. This study presents a comparative analysis of various versions of the YOLO (You...
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MDPI AG
2025-01-01
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| author | Christine Bukola Asaju Pius Adewale Owolawi Chuling Tu Etienne Van Wyk |
| author_facet | Christine Bukola Asaju Pius Adewale Owolawi Chuling Tu Etienne Van Wyk |
| author_sort | Christine Bukola Asaju |
| collection | DOAJ |
| description | Cloud-based license plate recognition (LPR) systems have emerged as essential tools in modern traffic management and security applications. Determining the best approach remains paramount in the field of computer vision. This study presents a comparative analysis of various versions of the YOLO (You Only Look Once) object detection models, namely, YOLO 5, 7, 8, and 9, applied to LPR tasks in a cloud computing environment. Using live video, we performed experiments on YOLOv5, YOLOv7, YOLOv8, and YOLOv9 models to detect number plates in real time. According to the results, YOLOv8 is reported the most effective model for real-world deployment due to its strong cloud performance. It achieved an accuracy of 78% during cloud testing, while YOLOv5 showed consistent performance with 71%. YOLOv7 performed poorly in cloud testing (52%), indicating potential issues, while YOLOv9 reported 70% accuracy. This tight alignment of results shows consistent, although modest, performance across scenarios. The findings highlight the evolution of the YOLO architecture and its impact on enhancing LPR accuracy and processing efficiency. The results provide valuable insights into selecting the most appropriate YOLO model for cloud-based LPR systems, balancing the trade-offs between real-time performance and detection precision. This research contributes to advancing the field of intelligent transportation systems by offering a detailed comparison that can guide future implementations and optimizations of LPR systems in cloud environments. |
| format | Article |
| id | doaj-art-38b6978b220945e496ca1a2a43363ad2 |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-38b6978b220945e496ca1a2a43363ad22025-01-24T13:35:18ZengMDPI AGInformation2078-24892025-01-011615710.3390/info16010057Cloud-Based License Plate Recognition: A Comparative Approach Using You Only Look Once Versions 5, 7, 8, and 9 Object DetectionChristine Bukola Asaju0Pius Adewale Owolawi1Chuling Tu2Etienne Van Wyk3Computer Systems Engineering, Tshwane University of Technology, Pretoria 0001, South AfricaComputer Systems Engineering, Tshwane University of Technology, Pretoria 0001, South AfricaComputer Systems Engineering, Tshwane University of Technology, Pretoria 0001, South AfricaComputer Systems Engineering, Tshwane University of Technology, Pretoria 0001, South AfricaCloud-based license plate recognition (LPR) systems have emerged as essential tools in modern traffic management and security applications. Determining the best approach remains paramount in the field of computer vision. This study presents a comparative analysis of various versions of the YOLO (You Only Look Once) object detection models, namely, YOLO 5, 7, 8, and 9, applied to LPR tasks in a cloud computing environment. Using live video, we performed experiments on YOLOv5, YOLOv7, YOLOv8, and YOLOv9 models to detect number plates in real time. According to the results, YOLOv8 is reported the most effective model for real-world deployment due to its strong cloud performance. It achieved an accuracy of 78% during cloud testing, while YOLOv5 showed consistent performance with 71%. YOLOv7 performed poorly in cloud testing (52%), indicating potential issues, while YOLOv9 reported 70% accuracy. This tight alignment of results shows consistent, although modest, performance across scenarios. The findings highlight the evolution of the YOLO architecture and its impact on enhancing LPR accuracy and processing efficiency. The results provide valuable insights into selecting the most appropriate YOLO model for cloud-based LPR systems, balancing the trade-offs between real-time performance and detection precision. This research contributes to advancing the field of intelligent transportation systems by offering a detailed comparison that can guide future implementations and optimizations of LPR systems in cloud environments.https://www.mdpi.com/2078-2489/16/1/57license plate recognitioncloud-basedYou Only Look Oncetraffic management |
| spellingShingle | Christine Bukola Asaju Pius Adewale Owolawi Chuling Tu Etienne Van Wyk Cloud-Based License Plate Recognition: A Comparative Approach Using You Only Look Once Versions 5, 7, 8, and 9 Object Detection Information license plate recognition cloud-based You Only Look Once traffic management |
| title | Cloud-Based License Plate Recognition: A Comparative Approach Using You Only Look Once Versions 5, 7, 8, and 9 Object Detection |
| title_full | Cloud-Based License Plate Recognition: A Comparative Approach Using You Only Look Once Versions 5, 7, 8, and 9 Object Detection |
| title_fullStr | Cloud-Based License Plate Recognition: A Comparative Approach Using You Only Look Once Versions 5, 7, 8, and 9 Object Detection |
| title_full_unstemmed | Cloud-Based License Plate Recognition: A Comparative Approach Using You Only Look Once Versions 5, 7, 8, and 9 Object Detection |
| title_short | Cloud-Based License Plate Recognition: A Comparative Approach Using You Only Look Once Versions 5, 7, 8, and 9 Object Detection |
| title_sort | cloud based license plate recognition a comparative approach using you only look once versions 5 7 8 and 9 object detection |
| topic | license plate recognition cloud-based You Only Look Once traffic management |
| url | https://www.mdpi.com/2078-2489/16/1/57 |
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