关键词: 孪生神经网络, 分类器, 空间变换器网络, 交通标志识别

Abstract: Siamese neural network consists of twin networks which share the same parameters, and can map nonlinear data with high dimension onto a low dimension and easy separable feature space. By taking use of its excellent performance on similarity computing, an efficient classifier based on Siamese network is proposed. Convolutional Neural Networks（CNNs） are used as building blocks, combined with max-pooling and dropout techniques, to construct a multi-scale CNNs for feature extraction. A spatial transformer network is used as a supplementary to promote the accuracy of the classifier. Through experiments on traffic sign data GTSRB, the classifier obtains 99.40% accuracy rate for this recognition benchmark. The proposed method for classification has virtues of simple structure, high accuracy, short training and fast recognition.

Key words: Siamese network, classifier, spatial transformer networks, traffic sign recognition