关键词: 野外车辆信号分类, 4通道声阵列输入, Inception结构, 注意力机制, 多分支特征提取, 多分支多维度特征融合

Abstract: Aiming at the problem that single channel vehicle acoustic signal is seriously affected by wind noise and has low classification performance, a one-dimensional convolutional neural network model FVC-CNN (convolutional neural network for field vehicle classification, FVC-CNN) based on four channel synchronous acquisition signal of acoustic array is proposed in this paper. The model uses the idea of weighted average of attention mechanism to improve the structure of Inception network. As the input layer, it extracts the features of four channel acoustic signals with different time scales to suppress noise interference. According to the distribution characteristics of different vehicle acoustic signals, three feature extraction networks, SWNet, LWNet, and TNet, are trained to extract the characteristics of the corresponding vehicle, finally, the extracted features are fused with multi branches and multi dimensions for classification. Verified on the same data set, the experimental results show that the total recognition rate of FVC-CNN model can reach 94.22%, which is 14.08% higher than the traditional method, and the classification effect is better.

Key words: field vehicle signal classification, four channel acoustic array input, Inception structure, attention mechanism, multi branch feature extraction, multi-branch and multi-dimensional feature fusion