Vehicle classification method based on convolution neural network

A convolutional neural network and classification method technology, applied in the field of machine learning and computer vision, can solve the problem of low accuracy of vehicle type recognition, achieve good vehicle classification accuracy, high flexibility and universality, and improve training time Effect

Inactive Publication Date: 2016-12-07
XIAN UNIV OF TECH
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Problems solved by technology

[0005] The purpose of the present invention is to provide a vehicle classification method based on convo

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  • Vehicle classification method based on convolution neural network
  • Vehicle classification method based on convolution neural network
  • Vehicle classification method based on convolution neural network

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Embodiment Construction

[0032] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0033] A kind of vehicle classification method based on the convolutional neural network of the present invention is faster under the computer Ubuntu system than under the windows system, so the present invention is explained by taking the Ubuntu system as an example.

[0034] Preparation:

[0035] Build a caffe deep learning framework platform under the computer Ubuntu system.

[0036] Build a convolutional neural network model, such as figure 1 Shown:

[0037] a: Determine the number of layers of the convolutional neural network: the convolutional neural network has a total of 8 layers, the first 5 layers are convolutional layers used for convolution and downsampling, and the last 3 layers are fully-connected layers (full-connected) ), the output output of the last fully connected layer is a SVM (Support Vector Machine, Support Vector Ma...

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Abstract

The invention discloses a vehicle classification method based on a convolution neural network. The vehicle classification method is concretely implemented according to the following steps that step 1: learning samples are acquired, and category tags are marked on the samples; step 2: the acquired learning samples act as training data to train a convolution neural network model so that excellent network model parameters are obtained; step 3: the characteristics of training data are extracted by using the trained convolution neural network model, and tenfold cross training is performed by using a support vector machine classification model constructed by a liblinear classifier so that the support vector machine classification model is obtained; and step 4: the characteristics of vehicle models under classification are extracted by suing the convolution neural network model, and then the vehicle categories to which vehicle images under classification belong are obtained by using the support vector machine classification model. All the connection layer output of the convolution neural network is used as the characteristic representation of the vehicle images and then the vehicle images are classified by using a SVM classifier so that the great accuracy of vehicle classification can be obtained.

Description

technical field [0001] The invention belongs to the technical field of machine learning and computer vision, and in particular relates to a vehicle classification method based on a convolutional neural network. Background technique [0002] With the continuous improvement of social living standards, the number of automobiles as a means of transportation shows a trend of rapid growth. But at the same time, it also brings a series of traffic problems such as traffic accidents and congestion, which brings huge challenges to traffic supervision. [0003] Vehicle recognition is an important part of intelligent transportation system. With the development of pattern recognition, image processing and computer vision technology, vehicle recognition technology based on image processing has received more and more attention. Especially in highway safety management, vehicle type identification plays an important role in traffic flow management, expressway toll collection and detection ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/02
CPCG06N3/02G06V20/584G06V2201/08G06F18/2411
Inventor 张二虎李敬段刚龙
Owner XIAN UNIV OF TECH
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