Recognition Of Vehicle Under Complex Background Based On Deep Learning

With the rapid growth of automobile ownership and the wide application of video surveillance technology,fine-grained vehicle recognition,as a key component of intelligent transportation system,has attracted extensive attention from researchers.Fine-grained vehicle models have small differences in types and variety of appearances.At the same time,the environment,illumination,noise and other factors contained in the image are easy to interfere with the feature extraction and recognition.Therefore,identifying fine-grained vehicle in a complex background faces more challenges.Unlike traditional methods that rely on artificial design features,Convolutional Neural Networks(CNN),which contains thousands of parameters,is trained autonomously in a data-driven way,with enhanced feature extraction and characterization capabilities.In this paper,the recognition of fine-grained vehicle models under complex background is studied based on deep convolutional neural network.The main tasks completed are as follows:Deep convolutional neural networks is optimized by batch normalization,gradient descent acceleration algorithm and loss function.On the basis of in-depth study on the structure,characteristics and training process,a fine-grained vehicle recognition network with 10 layers and a dataset containing 27 similar vehicle types are constructed.Meanwhile,the research optimizes the deep convolutional neural networks from three aspects,and verifies the feasibility of the optimization strategy in the process of fine-grained vehicle identification.Using batch normalization,the data distribution obeys the standard normal distribution,which avoids the deviation of internal transmission and reduces the complexity of network training.Due to the adaptive optimization algorithm,the gradient descent algorithm is prevented from falling into the local optimal,so that the output is closer to the real value.Comparing and analyzing the influence of square-loss,exp-loss and cross-entropy-loss on the training effect,the appropriate objective function is selected to speed up convergence.In the negative part of ReLU,an expression containing adaptive learning parameters is added to activate the output with negative values,which enhances the expression ability of the deep convolutional neural network to the negative input.The improved function EPReLU pushes mean unit activations closer to zero,effectively reduces the bias shift of data between layers and improves the learning speed of deep neural networks.Identifying27 types of fine-grained vehicle models,the accuracy of DCNN with this function reachs98.14%.Softmax layer in DCNN is improved by combining support vector machine(SVM),that avoids over-fitting for the purpose of minimizing risk.DCNN is adopted as the initial model for vehicle models recognition and training.When the training accuracy reachs the recognition threshold,FC1 layer features are extracted and input into SVM to complete further training.The classifier can not only maintain the feature extraction advantage of DCNN,but also avoid the overtraining of Softmax on correctly classified samples.In fine-grained vehicle recognition under complex background,the ability of feature learning of DCNN is more vividly illustrated by visualizing the output feature maps and convolution kernels weight.It is found that compared with SVM,Bo F and unimproved DCNN,the fine vehicle types identification precision of softmax-svm reachs 97.78%,improving by 54.04%,10.19% and 2.27%,respectively.In terms of time,softmax-svm takes only 31% of DCNN.