关键词: 细粒度车型识别, 残差网络, 独立组件, 中心损失

Abstract:

Aiming at the problem of low recognition rate between the same car series in fine-grained models, in order to enhance the representational ability of the convolutional neural network, a ResNet model with integrated Independent Components（IC-ResNet） is proposed. Firstly, ResNet is optimized to reduce the loss of feature information by improving the lower sampling layer, and then the center loss function and Softmax loss function are combined to improve the class cohesion of the model. Then an IC layer is introduced in front of the convolution layer to obtain relatively independent neurons, enhance network independence, and improve the feature representation ability of the model, so as to achieve more accurate classification of fine-grained vehicle models. The experiment shows that the model recognition accuracy on the Stanford cars-196 data set is 94.7%, which achieves the optimal effect compared with other models and verifies the effectiveness of the recognition model of this model.

Key words: fine-grained vehicle recognition, Residual Network（ResNet）, Independent Component（IC）, central loss