Automatic motor vehicle number plate recognition
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024.
| Հիմնական հեղինակներ: | , , , |
|---|---|
| Այլ հեղինակներ: | |
| Ձևաչափ: | Թեզիս |
| Լեզու: | English |
| Հրապարակվել է: |
Brac University
2024
|
| Խորագրեր: | |
| Առցանց հասանելիություն: | http://hdl.handle.net/10361/22894 |
| id |
10361-22894 |
|---|---|
| record_format |
dspace |
| spelling |
10361-228942024-05-20T21:03:54Z Automatic motor vehicle number plate recognition Saha, Krishno Ishrak, Parvez Shovon, Jahid Hossian Abir, Alinur Rahman Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University ResNet50 YOLOv5 YOLOv8 Automated number plate detection Feature extraction NPR Vehicle number plate Computer simulation Transportation engineering Traffic engineering Image processing--Digital techniques This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 43-46). The purpose of this initiative is to develop automatic motor vehicle number plate recognition (Bangla) using machine learning, identifying and taking out the numbers of license plates from photos. By using this system we intend to help the traffic control system in detecting any issue within a few moments. Moreover, collecting tolls and enforcement of law can be implemented with this number plate recognition system. Various object detection models have been used in this in various suggested methods to identify and recognize number plates, optical character recognition and license plate detection make up the system’s three basic building blocks. YOLOv8, YOLOv7, YOLOv5, VGG16, RESNET50, DETR, VGG16 are the models used in this project. Object detection models are used to detect the number plate of a vehicle from the images. That is how the method will be able to successfully recognize and detect the number plate. The precision, recall and mAP value of YOLOv8 is 96.4%, 84.8%, 92.9% respectively. For YOLOv7 it is 61.1%, 46%, 46.5% respectively. For YOLOv5 it is 98.1%, 12.1%, 17.4% respectively. DETR is 6.5%, 7.5%, 8.32% respectively. For VGG16 the test accuracy is 90.14% and for ResNet50 it is 89.91%. Additionally, this system will be implemented within the web. So by using a phone camera the car number plates would be detected with a device like a mobile phone. To sum up, the number plate detection system has the ability to detect, identify and be able to save the information and will help provide a reliable management system for traffic and capturing fraud and indiscipline in the traffic control system. Krishno Saha Parvez Ishrak Jahid Hossian Shovon Alinur Rahman Abir B.Sc in Computer Science and Engineering 2024-05-20T09:30:26Z 2024-05-20T09:30:26Z ©2024 2024-01 Thesis ID: 19101271 ID: 19101266 ID: 22101911 ID: 19101055 http://hdl.handle.net/10361/22894 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 55 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
ResNet50 YOLOv5 YOLOv8 Automated number plate detection Feature extraction NPR Vehicle number plate Computer simulation Transportation engineering Traffic engineering Image processing--Digital techniques |
| spellingShingle |
ResNet50 YOLOv5 YOLOv8 Automated number plate detection Feature extraction NPR Vehicle number plate Computer simulation Transportation engineering Traffic engineering Image processing--Digital techniques Saha, Krishno Ishrak, Parvez Shovon, Jahid Hossian Abir, Alinur Rahman Automatic motor vehicle number plate recognition |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Saha, Krishno Ishrak, Parvez Shovon, Jahid Hossian Abir, Alinur Rahman |
| format |
Thesis |
| author |
Saha, Krishno Ishrak, Parvez Shovon, Jahid Hossian Abir, Alinur Rahman |
| author_sort |
Saha, Krishno |
| title |
Automatic motor vehicle number plate recognition |
| title_short |
Automatic motor vehicle number plate recognition |
| title_full |
Automatic motor vehicle number plate recognition |
| title_fullStr |
Automatic motor vehicle number plate recognition |
| title_full_unstemmed |
Automatic motor vehicle number plate recognition |
| title_sort |
automatic motor vehicle number plate recognition |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/22894 |
| work_keys_str_mv |
AT sahakrishno automaticmotorvehiclenumberplaterecognition AT ishrakparvez automaticmotorvehiclenumberplaterecognition AT shovonjahidhossian automaticmotorvehiclenumberplaterecognition AT abiralinurrahman automaticmotorvehiclenumberplaterecognition |
| _version_ |
1814309154037170176 |