Traffic Surveillance using Vehicle License Plate Detection and Recognition in Bangladesh

Computer vision coupled with Deep Learning (DL) techniques bring out a substantial prospect in the field of traffic control, monitoring and law enforcing activities. This paper presents a YOLOv4 object detection model in which the Convolutional Neural Network (CNN) is trained and tuned for detecting...

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Bibliographic Details
Published inarXiv.org
Main Authors Md Saif Hassan Onim, Muhaiminul Islam Akash, Haque, Mahmudul, Raiyan Ibne Hafiz
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 03.12.2020
Subjects
Artificial neural networks
Character recognition
Computer Science - Computer Vision and Pattern Recognition
Computer vision
Frames per second
Graphical user interface
License plates
Object recognition
Traffic control
Traffic surveillance
Vehicle identification
Bangladesh
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Summary:Computer vision coupled with Deep Learning (DL) techniques bring out a substantial prospect in the field of traffic control, monitoring and law enforcing activities. This paper presents a YOLOv4 object detection model in which the Convolutional Neural Network (CNN) is trained and tuned for detecting the license plate of the vehicles of Bangladesh and recognizing characters using tesseract from the detected license plates. Here we also present a Graphical User Interface (GUI) based on Tkinter, a python package. The license plate detection model is trained with mean average precision (mAP) of 90.50% and performed in a single TESLA T4 GPU with an average of 14 frames per second (fps) on real time video footage.
ISSN:2331-8422
DOI:10.48550/arxiv.2012.02218