| Popis: |
The difference between rail fastener defect images is very subtle, and the existing classification model has the problem of high false positive rate (FPR)and false negative rate (FNR). We proposed a multimodal bilinear convolutional neural network (MB-CNN) model. The new model uses two different networks, VGG16 network and ResNet-50 network, to form a multi-mode bilinear structure. In VGG16 network, multi-scale feature fusion operation is added to increase the diversity of features extracted from the model. The attention mechanism was added in two different networks to enhance the model’s ability to pay attention to the rail fastener area. The experimental results show that the classification accuracy of the MB-CNN model is 99.67%, and the FPR of normal classification of coupler is 0%. Compared with the bilinear convolutional neural network (B-CNN) model, the FNR of normal category was reduced by 0.98%, and the FNR of missing category was reduced by 0.99%. The experimental results show that the MB-CNN model is superior to the general classification network and has better performance in each category classification, which verifies the effectiveness of the improved model. |
