MLPR: YOLOv3 for Real-Time License Plate Recognition in Moroccan Video Streams
Automated License Plate Detection and Recognition (ALPR) is a technology that identifies and reads license plates in transportation and surveillance systems. However, ALPR is a challenging task due to several factors, including differences in vehicle license plates between countries, a lack of publi...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10848072/ |
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| author | Hatim Derrouz Hamza Alami Reda Rabie |
| author_facet | Hatim Derrouz Hamza Alami Reda Rabie |
| author_sort | Hatim Derrouz |
| collection | DOAJ |
| description | Automated License Plate Detection and Recognition (ALPR) is a technology that identifies and reads license plates in transportation and surveillance systems. However, ALPR is a challenging task due to several factors, including differences in vehicle license plates between countries, a lack of public datasets, environmental factors that can decrease the performance of recognition models, and challenging lighting conditions for vehicle license plates. In this work, we present an ALPR system for real-time applications specifically designed to detect and recognize Moroccan license plates. Our contribution is threefold. First, we employ the YOLOv3 algorithm to extract valuable features from input scenes and detect vehicles. Second, we track vehicles and assign a unique id to each detected vehicle. Finally, we detect and recognize the plates of tracked vehicles based on computer vision techniques and a voting system. The latter is a post-processing step that selects the most consistent content as the final prediction. Furthermore, we collected and annotated a set of videos that contain vehicles with Moroccan license plates in various conditions. The collected data is used to assess the performance of our system. The obtained results demonstrate that our system achieves an accuracy of 99.19% and 95.62% for detection and recognition, respectively. |
| format | Article |
| id | doaj-art-fbf98792647f458c8877ec1faf2057af |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fbf98792647f458c8877ec1faf2057af2025-02-06T00:00:47ZengIEEEIEEE Access2169-35362025-01-0113219562196510.1109/ACCESS.2025.353253810848072MLPR: YOLOv3 for Real-Time License Plate Recognition in Moroccan Video StreamsHatim Derrouz0https://orcid.org/0000-0003-0619-6190Hamza Alami1https://orcid.org/0000-0001-6945-6098Reda Rabie2https://orcid.org/0000-0002-4138-3234Laboratory of Research in Informatics (LARI), Ibn Tofail University, Kenitra, MoroccoLaboratory of Informatics, Signals, Automatics, and Cognitivism (LISAC), Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah University, Fes, MoroccoMAScIR Fondation, Mohammed VI Polytechnic University, Ben Guerir, MoroccoAutomated License Plate Detection and Recognition (ALPR) is a technology that identifies and reads license plates in transportation and surveillance systems. However, ALPR is a challenging task due to several factors, including differences in vehicle license plates between countries, a lack of public datasets, environmental factors that can decrease the performance of recognition models, and challenging lighting conditions for vehicle license plates. In this work, we present an ALPR system for real-time applications specifically designed to detect and recognize Moroccan license plates. Our contribution is threefold. First, we employ the YOLOv3 algorithm to extract valuable features from input scenes and detect vehicles. Second, we track vehicles and assign a unique id to each detected vehicle. Finally, we detect and recognize the plates of tracked vehicles based on computer vision techniques and a voting system. The latter is a post-processing step that selects the most consistent content as the final prediction. Furthermore, we collected and annotated a set of videos that contain vehicles with Moroccan license plates in various conditions. The collected data is used to assess the performance of our system. The obtained results demonstrate that our system achieves an accuracy of 99.19% and 95.62% for detection and recognition, respectively.https://ieeexplore.ieee.org/document/10848072/Automatic license plate detection and recognitionMoroccan license plate datasetcomputer visionYOLOv3 deep neural network |
| spellingShingle | Hatim Derrouz Hamza Alami Reda Rabie MLPR: YOLOv3 for Real-Time License Plate Recognition in Moroccan Video Streams IEEE Access Automatic license plate detection and recognition Moroccan license plate dataset computer vision YOLOv3 deep neural network |
| title | MLPR: YOLOv3 for Real-Time License Plate Recognition in Moroccan Video Streams |
| title_full | MLPR: YOLOv3 for Real-Time License Plate Recognition in Moroccan Video Streams |
| title_fullStr | MLPR: YOLOv3 for Real-Time License Plate Recognition in Moroccan Video Streams |
| title_full_unstemmed | MLPR: YOLOv3 for Real-Time License Plate Recognition in Moroccan Video Streams |
| title_short | MLPR: YOLOv3 for Real-Time License Plate Recognition in Moroccan Video Streams |
| title_sort | mlpr yolov3 for real time license plate recognition in moroccan video streams |
| topic | Automatic license plate detection and recognition Moroccan license plate dataset computer vision YOLOv3 deep neural network |
| url | https://ieeexplore.ieee.org/document/10848072/ |
| work_keys_str_mv | AT hatimderrouz mlpryolov3forrealtimelicenseplaterecognitioninmoroccanvideostreams AT hamzaalami mlpryolov3forrealtimelicenseplaterecognitioninmoroccanvideostreams AT redarabie mlpryolov3forrealtimelicenseplaterecognitioninmoroccanvideostreams |