Research On Fine-grained Vehicle Recognition Method Based On Deep Convolutional Neural Network

With the rapid development of our country’s economy and the further improvement of transportation and other infrastructure,the number of cars in each city is also growing rapidly.How to effectively manage a large number of cars and complex traffic conditions has become a major challenge for the management department.Vehicle recognition is applied in many fields,such as intelligent transportation,unmanned driving,and commercial applications.Due to the large number of vehicle types,brands and models,and the small differences between different types of vehicles,fine-grained recognition of vellicle models is very necessary.It has a wide range of applications in smart transportation,tracking vehicles,and taking pictures to identify vehicles.At present,the classification methods of vehicle mainly include traditional methods and methods based on deep learning.Traditional image recognition methods usually extract features from images manually,and then classify them by classifiers.With the increasing update of deep learning technology,convolutional neural network,as an important branch,has been widely used in the field of image recognition and has achieved good results.However,the application of fine-grained recognition still needs to be studied in depth.Therefore,this paper studies fine-grained car recognition based on deep corbvolutional neural networks to improve the accuracy of recognition.The work of thas paper is mainly embodied in the following two aspects:First,in view of the relatovely single feature extraction of ordinary neural networks and the problem of network degradation,a fine-grained vehicle recognition method based on pro-Resnet is proposed.The residual structure is used to solve the problem of network degradation as the number of network layers deepens,and the Inception module is introduced into it.Diferent features of the car image are exttracted through parallel conlvolution kernels of different sizes,and finally aggregated.Different sizes of receptive fields are used to obtain features of different scales.which further improves the recognition accuracy.Second,to solve the problem that the difference between different categories of fine-grained images is small,and the distance between different features is relatively close,it is difficult to accurately identify the problem,and a vehicle recognition method based on ISC-Resnet is proposed.Optimize the loss function part,combine the softmax cross-entropy loss function and the central loss function,and jointly supervise the network model to improve the compactness within the class,thereby improving the dispersion between classes,and effectively distinguishing different categories.So as to achieve the effect of improving the recognition accuracy rate as a whole.This experiment uses Stanford cars database stanford cars as the data source,and the test set recognition accuracy rate is 92.3%.The experimental results show that the method in this paper can effectively extract vehicle features and improve the recognition accuracy of the network.