| 基于联合层特征的卷积神经网络在车标识别中的应用 |
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| 引用本文： | 张力,张洞明,郑宏. 基于联合层特征的卷积神经网络在车标识别中的应用[J]. 计算机应用, 2016, 36(2): 444-448. DOI: 10.11772/j.issn.1001-9081.2016.02.0444 |
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| 作者姓名： | 张力 张洞明 郑宏 |
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| 作者单位： | 1. 武汉大学 电子信息学院, 武汉 430072;2. 湖北省视觉感知与智能交通技术研发中心, 武汉 430072 |
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| 基金项目： | 国家973计划项目(2012CB719905)。 |
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| 摘 要： | 针对现有智能交通系统仅仅通过车牌信息获取车辆信息存在不准确的情况,提出一种基于联合层特征的卷积神经网络(Multi-CNN)进行车标识别。该方法将通过卷积神经网络中不同层提取的特征联合起来,一起作为全连接层的输入,训练获得分类器。通过理论分析和实验表明,与传统的卷积神经网络训练获得的分类器相比,Multi-CNN方法能够减少训练所需计算量,同时将车标识别准确率提升至98.7%。
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| 关 键 词： | 深度学习 卷积神经网络 联合特征 车标识别 |
| 收稿时间： | 2015-08-29 |
| 修稿时间： | 2015-09-14 |
Vehicle logo recognition using convolutional neural network combined with multiple layer feature |
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| ZHANG Li,ZHANG Dongming,ZHENG Hong. Vehicle logo recognition using convolutional neural network combined with multiple layer feature[J]. Journal of Computer Applications, 2016, 36(2): 444-448. DOI: 10.11772/j.issn.1001-9081.2016.02.0444 |
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| Authors: | ZHANG Li ZHANG Dongming ZHENG Hong |
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| Affiliation: | 1. School of Electronic Information, Wuhan University, Wuhan Hubei 430072, China;2. Hubei Research and Development Center of Vision Perception and Intelligent Transportation Technology, Wuhan Hubei 430072, China |
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| Abstract: | Concerning the inaccurate vehicle information captured by the license plate of the existing intelligent traffic system, a vehicle logo recognition method based on the Convolutional Neural Network (CNN) combined with different layer features, namely Multi-CNN, was proposed. Firstly, the different layer features were obtained using CNN. Secondly, the extracted features were joined together and regarded as the input of the fully connected layer to get classifiers. The theoretical analysis and simulation results show that, compared with the traditional method, Multi-CNN method can reduce the training time and increase the recognition accuracy to 98.7%. |
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| Keywords: | deep learning Convolutional Neural Network(CNN) multiple feature vehicle logo recognition |
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