论文标题
孟加拉国使用车牌检测和认可的交通监视
Traffic Surveillance using Vehicle License Plate Detection and Recognition in Bangladesh
论文作者
论文摘要
计算机视觉加上深度学习（DL）技术，在交通控制，监视和执行活动领域带来了很大的前景。本文介绍了Yolov4对象检测模型，其中对卷积神经网络（CNN）进行了训练和调整，用于检测孟加拉国车辆车辆的车牌，并使用检测到的车牌中的Tesseract识别角色。在这里，我们还基于python软件包TKINTER提供了图形用户界面（GUI）。车牌检测模型的平均平均精度（MAP）为90.50％，并在单个Tesla T4 GPU中进行，实时视频录像中平均每秒14帧（FPS）。
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.
