DETECTION AND RECOGNITION OF IRAQI LICENSE PLATES USING CONVOLUTIONAL NEURAL NETWORKS
Due to the large population of motorway users in the country of Iraq, various approaches have been adopted to manage queues such as implementation of traffic lights, avoidance of illegal parking, amongst others. However, defaulters are recorded daily, hence the need to develop a mean of identifying...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Zakho
2025-01-01
|
| Series: | Science Journal of University of Zakho |
| Subjects: | |
| Online Access: | https://sjuoz.uoz.edu.krd/index.php/sjuoz/article/view/1344 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841558853061181440 |
|---|---|
| author | Mohammed Hayder Abbas Zeina Mueen Mohammed |
| author_facet | Mohammed Hayder Abbas Zeina Mueen Mohammed |
| author_sort | Mohammed Hayder Abbas |
| collection | DOAJ |
| description |
Due to the large population of motorway users in the country of Iraq, various approaches have been adopted to manage queues such as implementation of traffic lights, avoidance of illegal parking, amongst others. However, defaulters are recorded daily, hence the need to develop a mean of identifying these defaulters and bring them to book. This article discusses the development of an approach of recognizing Iraqi licence plates such that defaulters of queue management systems are identified. Multiple agencies worldwide have quickly and widely adopted the recognition of a vehicle license plate technology to expand their ability in investigative and security matters. License plate helps detect the vehicle's information automatically rather than a long time consuming manually gathering for the information. In this article, transfer learning is employed to train two distinct YOLOv8 models for enhanced automatic number plate recognition (ANPR). This approach leverages the strengths of YOLOv8 in handling complex patterns and variations in license plate designs, showcasing significant promise for real-world applications in vehicle identification and law enforcement.
|
| format | Article |
| id | doaj-art-9348fc26ceb640f58d0e4aa38594116f |
| institution | Kabale University |
| issn | 2663-628X 2663-6298 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | University of Zakho |
| record_format | Article |
| series | Science Journal of University of Zakho |
| spelling | doaj-art-9348fc26ceb640f58d0e4aa38594116f2025-01-06T02:25:25ZengUniversity of ZakhoScience Journal of University of Zakho2663-628X2663-62982025-01-0113110.25271/sjuoz.2025.13.1.1344DETECTION AND RECOGNITION OF IRAQI LICENSE PLATES USING CONVOLUTIONAL NEURAL NETWORKSMohammed Hayder Abbas0Zeina Mueen Mohammed1Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq Due to the large population of motorway users in the country of Iraq, various approaches have been adopted to manage queues such as implementation of traffic lights, avoidance of illegal parking, amongst others. However, defaulters are recorded daily, hence the need to develop a mean of identifying these defaulters and bring them to book. This article discusses the development of an approach of recognizing Iraqi licence plates such that defaulters of queue management systems are identified. Multiple agencies worldwide have quickly and widely adopted the recognition of a vehicle license plate technology to expand their ability in investigative and security matters. License plate helps detect the vehicle's information automatically rather than a long time consuming manually gathering for the information. In this article, transfer learning is employed to train two distinct YOLOv8 models for enhanced automatic number plate recognition (ANPR). This approach leverages the strengths of YOLOv8 in handling complex patterns and variations in license plate designs, showcasing significant promise for real-world applications in vehicle identification and law enforcement. https://sjuoz.uoz.edu.krd/index.php/sjuoz/article/view/1344Automatic number plate recognitionmachine learningtransfer learningqueue managementYOLOv8 |
| spellingShingle | Mohammed Hayder Abbas Zeina Mueen Mohammed DETECTION AND RECOGNITION OF IRAQI LICENSE PLATES USING CONVOLUTIONAL NEURAL NETWORKS Science Journal of University of Zakho Automatic number plate recognition machine learning transfer learning queue management YOLOv8 |
| title | DETECTION AND RECOGNITION OF IRAQI LICENSE PLATES USING CONVOLUTIONAL NEURAL NETWORKS |
| title_full | DETECTION AND RECOGNITION OF IRAQI LICENSE PLATES USING CONVOLUTIONAL NEURAL NETWORKS |
| title_fullStr | DETECTION AND RECOGNITION OF IRAQI LICENSE PLATES USING CONVOLUTIONAL NEURAL NETWORKS |
| title_full_unstemmed | DETECTION AND RECOGNITION OF IRAQI LICENSE PLATES USING CONVOLUTIONAL NEURAL NETWORKS |
| title_short | DETECTION AND RECOGNITION OF IRAQI LICENSE PLATES USING CONVOLUTIONAL NEURAL NETWORKS |
| title_sort | detection and recognition of iraqi license plates using convolutional neural networks |
| topic | Automatic number plate recognition machine learning transfer learning queue management YOLOv8 |
| url | https://sjuoz.uoz.edu.krd/index.php/sjuoz/article/view/1344 |
| work_keys_str_mv | AT mohammedhayderabbas detectionandrecognitionofiraqilicenseplatesusingconvolutionalneuralnetworks AT zeinamueenmohammed detectionandrecognitionofiraqilicenseplatesusingconvolutionalneuralnetworks |