关键词: 卷积神经网络（CNN）, 支持向量数据描述（SVDD）, 异常检测

Abstract: In the train running system, the accurate access to the catenary pillar number is the prerequisite for the intelligent monitoring of the catenary pillar state, to determine whether the abnormal state of the pillar has become an urgent problem to be solved. Because of the difficulty in obtaining and marking the abnormal data in the actual situation, the traditional pattern recognition method has the limitation of only a large number of normal classes non-equilibrium data processing. Through the semi-supervised learning idea, the improved Lenet-5 neural network and support vector data description are used to detect anomalies with only a few markers, and the depth image features are learned and extracted by the neural network. The support vector data description algorithm is used to train these image features, and obtain the normal domain model of the column number image feature, finally, determine whether the new catenary pillar number is abnormal.

Key words: Convolution Neural Network（CNN）, Support Vector Data Description（SVDD）, anomaly detection