Fine Vehicle Recognition Based on Feature Enhancement and Grouping Module
In order to solve the problems of many types of vehicles,small differences,complex models and low recognition ac-curacy,a fine vehicle recognition method based on feature enhancement and grouping module is proposed,which is improved on the basis of ResNet network.The attention mechanism of multi-scale channel domain and spatial domain is added to the convolution block to enhance the extraction of important features,and the multi-channel feature graphs are grouped and continuously optimized according to the grouping loss function.KL(Kullback-Leibler)divergence loss function and cross entropy loss function are com-bined by weighted method.The method is tested on Stanford cars-196 dataset and self-made dataset to verify the effectiveness of the proposed model.
fine vehicle identificationmulti-scaleattention mechanismfeature enhancementloss function
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