一种基于GMP-LeNet网络的车牌识别方法

车牌识别技术是智能交通管理系统的核心,对它的研究与开发具有重要的商业前景。传统的车牌字符识别方法存在特征提取复杂的问题,而卷积神经网络作为一种高效识别算法,对处理二维车牌图像具有独特的优越性。针对传统卷积神经网络LeNet-5识别车牌图像时,存在训练数据较少、全连接层参数冗余以及网络严重过拟合等一系列的问题,设计了一种全局中间值池化(GMP-LeNet)网络,其使用卷积层代替全连接层,利用Network In Network网络中的1*1卷积核进行通道降维,全局均值池化层直接将降维后的特征图馈送到输出层。实验证明,GMP-LeNet网络能有效抑制过拟合现象,并具有较快的识别速度和较高的鲁棒性,车牌识别率达到了98.5%。

License Plate Recognition Method Based on GMP-LeNet Network

As the core of intelligent traffic management system,the research of license plate recognition technology has important business prospects.The traditional license plate character recognition method has the problem of complex feature extraction.As an efficient recognition algorithm,convolution neural network has a unique superiority in dealing with two-dimensional license plate image.When the traditional convolution neural network LeNet-5 identifies the license plate image,there is a series of problems such as less training data,redundancy of the fully connection layer and over-fitting of the network.A global intermediate pool (GMP-LeNet) network was designed,which utilizes the convolution la-yer instead of the fully connection layer.The 1*1 convolution kernel learning from the NIN network is used to reduce channel dimension.Then the global mean pool layer feeds the feature graph to the output layer after the dimension reducing directly.Experiments show that GMP-LeNet network can suppress the over-fitting phenomenon effectively with a faster recognition speed and the higher robustness.The final license plate recognition rate is close to 98.5%.