Traffic Surveillance using Vehicle License Plate Detection and Recognition in Ba

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 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.

孟加拉国使用车牌检测和识别进行交通监控

计算机视觉与深度学习（DL）技术的结合为交通控制，监控和执法活动领域带来了广阔的前景。本文提出了一种YOLOv4目标检测模型，其中对卷积神经网络（CNN）进行了训练和调整，以检测孟加拉国车辆的车牌并使用tesseract从检测到的车牌中识别字符。.. 在这里，我们还展示了基于python软件包Tkinter的图形用户界面（GUI）。车牌检测模型以90.50％的平均平均精度（mAP）进行训练，并在单个TESLA T4 GPU中以实时视频片段平均每秒14帧（fps）的速度执行。 （阅读更多）