基于卷积神经网络的车型识别

Yongguo Ren, Shanzhen Lan
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引用次数: 10

摘要

车辆分析是智能应用中的一项重要任务,涉及到车型分类、车牌识别和车型识别。在这些任务中,MMR对LPR起着重要的补充作用。本文提出了一种利用卷积神经网络检测移动车辆和MMR的新框架。首先提取车辆正面视图图像,并将其输入卷积神经网络进行训练和测试。实验结果表明,该框架在车辆MMR上的识别准确率达到了98.7%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vehicle make and model recognition based on convolutional neural networks
Vehicle analysis is an important task in many intelligent applications, which involves vehicle-type classification(VTC), license-plate recognition(LPR) and vehicle make and model recognition(MMR). Among these tasks, MMR plays an important complementary role with respect to LPR. In this paper, we propose a novel framework to detect moving vehicle and MMR using convolutional neural networks. The frontal view of vehicle images first extracted and fed into convolutional neural networks for training and testing. The experimental results show that our proposed framework achieves favorable recognition accuracy 98.7% in terms of our vehicle MMR.
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