Traffic Surveillance using Vehicle License Plate Detection and Recognition in Bangladesh

TL;DR

This paper develops a real-time vehicle license plate detection and recognition system for Bangladesh using YOLOv4 and Tesseract, achieving high accuracy and frame rate on GPU, aiding traffic management and law enforcement.

Contribution

It introduces a YOLOv4-based license plate detection and Tesseract OCR recognition system tailored for Bangladeshi vehicles, with a GUI interface for practical deployment.

Findings

License plate detection with 90.50% mAP

Real-time processing at 14 fps on Tesla T4 GPU

Effective recognition of Bangladeshi vehicle plates

Abstract

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.