Traffic Surveillance using Vehicle License Plate Detection and Recognition in Bangladesh
This work provides an incremental solution for traffic surveillance and law enforcement in Bangladesh by automating license plate detection and recognition.
This paper developed a YOLOv4 object detection model trained to detect Bangladeshi vehicle license plates with a mean average precision (mAP) of 90.50%. The system also recognizes characters from detected plates using Tesseract and processes real-time video at 14 frames per second on a single TESLA T4 GPU.
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.