基于YOLO- V3和OCR方法的车牌检测与识别

R. Shashidhar, A. Manjunath, R. Santhosh Kumar, M. Roopa, S. Puneeth
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引用次数: 14

摘要

车牌自动识别系统是从给定的车辆图像中提取数据,并将其应用于安全、可靠和现代化的交通系统中,是众多信息系统中的一种。该项目的新颖之处在于,即使图像是模糊的,我们的系统也可以对给定图像进行去模糊处理,并进一步将其应用于机器学习模型。在提出的工作中,您只看一次[YOLO] V3模型的兴趣区域[ROI];实现了卷积神经网络(CNN)用于光学字符识别。在检测到ROI后,对其进行预处理步骤增强,然后将其馈送到CNN模型。建立了不同印度车牌字体的数据集,包含6439张不同的alpha-数字字符图像。准确度为91.5%。将提取和排序的车牌字符与印度RTO数据库进行交叉核对,并提供输入车辆图像所属RTO的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vehicle Number Plate Detection and Recognition using YOLO- V3 and OCR Method
Automatic Number Plate Recognition is one of the many information systems which is used data extraction from given vehicle image and apply the data for further usage in safe, secure and modernistic Transportation System. The Novelty of the project is that even if the image is blurred, our system can deblur the given image and apply it to the Machine Learning models further. In the proposed work, You Only Look Once [YOLO] V3 model for Region of Interest [ROI]; Convolution Neural Network [CNN] for optical character recognition was implemented. After the ROI is detected, it will be enhanced with pre-processing steps before it is fed to CNN model. Dataset of different Indian Number Plates’ Font was created, consisting of 6439 images of different alpha- numerical characters. Accuracy of 91.5 percent is obtained. The extracted and sorted characters of the number plate is cross- checked with the Indian RTO database and the information regarding which RTO the input vehicle image belongs to, is provided.
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