Aiming at the problem that the feature level fusion effect is not ideal in the vehicle detection and classification task at intersections,a phased feature fusion solution strategy was proposed.The principal component analysis was used to fuse the fea-tures in the time domain and frequency domain,and the advantages of the traditional long term memory network and convolutional neural network were combined to build a vehicle classification model.Experimental results show that the classification accuracy of the ConvBiLSTM model proposed can reach over 98％,and the highest F1 score of 98.76％is achieved.Experimental results effectively verify the necessity of feature fusion and the effectiveness of the improved classification model,and it provides a feasi-ble solution for solving the problem of vehicle detection classification at intersections.
关键词
多特征融合/时频分析/车型分类/车辆声信号/车辆检测/长短时记忆网络/卷积神经网络
Key words
multi feature fusion/time frequency analysis/vehicle classification/vehicle sound signal/vehicle inspection/long short-term memory network/convolutional neural network
维普
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