Fine-Grained Car Recognition Model Based on Semantic DCNN Features Fusion

As the deep convolution neural networks (DCNN) lack the ability of representation of semantic information,while visual differences between classes are small and concentrated on key semantic parts during the fine-grained categorization,this paper proposes a model based on fusing semantic information of DCNN features,which is composed of the detection sub-network and the classification sub-network.Firstly,by use of the detection sub-network we capture the definite position of car object and each semantic parts through Faster RCNN.Secondly,the classification sub-network extracts the overall car object features and semantic parts features of the object via DCNN,then processes the joint and fusion of features by using small kernel convolution.Finally,we obtain final recognition result through deep neural network.The recognition accuracy of our model is 78.74% in Stanford BMW-10 dataset,which is 13.39% higher than the VGG network method and 85.94% in the Stanford cars-197 dataset.And the recognition accuracy of the transfer learning models in BMVC car-types dataset is 98.27%,which is 3.77% higher than the best recognition result of the dataset.Experimental results show that our model avoids the dependence of the fine-grained car recognition on the positions of car object and semantic parts,with high recognition accuracy and versatility.