CN106203330A - A kind of vehicle classification method based on convolutional neural networks - Google Patents

A kind of vehicle classification method based on convolutional neural networks Download PDF

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CN106203330A
CN106203330A CN201610536734.0A CN201610536734A CN106203330A CN 106203330 A CN106203330 A CN 106203330A CN 201610536734 A CN201610536734 A CN 201610536734A CN 106203330 A CN106203330 A CN 106203330A
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张二虎
李敬
段刚龙
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Xian University of Technology
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    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
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Abstract

The invention discloses a kind of vehicle classification method based on convolutional neural networks, specifically implement according to following steps: step 1: obtain learning sample, and stamp class label to sample；Step 2: using the learning sample that gets as training data, training convolutional neural networks model, obtain outstanding network model's parameter；Step 3: use the convolutional neural networks model trained, extracts the feature of training data, and the support vector cassification model built with liblinear grader carries out ten folding cross-trainings, obtains support vector cassification model；Step 4: the feature of the auto model that use convolutional neural networks model extraction is to be sorted, then uses support vector cassification model, obtains the class of vehicle belonging to vehicle image to be sorted.The present invention utilizes the full articulamentum output of convolutional neural networks as the character representation of vehicle image, then utilizes SVM classifier to classify it, thus obtains preferable vehicle classification accuracy rate.

Description

A kind of vehicle classification method based on convolutional neural networks
Technical field
The invention belongs to machine learning and technical field of computer vision, be specifically related to a kind of based on convolutional neural networks Vehicle classification method.
Background technology
Along with social life level improves constantly, automobile is as a kind of vehicles, and quantity presents the trend of rapid growth. But also bring such as vehicle accident, a series of traffic problems such as block up, and brings huge challenge to traffic monitoring simultaneously.
Vehicle identification is the important component part of intelligent transportation system.Along with pattern recognition, image procossing and computer The development of vision technique, vehicle recongnition technique based on image procossing has obtained increasing concern.Especially at highway safety In management, the identification of type of vehicle is played at aspects such as traffic flow management, expressway tol lcollection and detection lorry illegal road occupations Important effect.
The speed of Classification and Identification and accuracy are two most important indexs of vehicle identification, for car based on image procossing Identify system, extract vehicle characteristics be to affect the most important factor of the two index, be the key of whole identification process.Vehicle The extraction of feature is affected by factors: but vehicle class does not has obvious distinguishing characteristics, the impact of weather, illumination Impact etc., this brings greatly challenge to the identification of type of vehicle.
Summary of the invention
It is an object of the invention to provide a kind of vehicle classification method based on convolutional neural networks, solve existing vehicle class The problem that the accuracy rate of type identification is low.
The technical solution adopted in the present invention is, a kind of vehicle classification method based on convolutional neural networks, specifically according to Following steps are implemented:
Step 1: obtain learning sample, and stamp class label to sample；
Step 2: learning sample step 1 got, as training data, training convolutional neural networks model, obtains excellent Elegant network model's parameter；
Step 3: use the convolutional neural networks model trained in step 2, extracts the feature of training data, uses The support vector cassification model that liblinear grader builds carries out ten folding cross-trainings, obtains support vector cassification mould Type；
Step 4: use the feature of the auto model that convolutional neural networks model extraction is to be sorted in step 2, then use The support vector cassification model that step 3 obtains, obtains the class of vehicle belonging to vehicle image to be sorted.
The feature of the present invention also resides in:
The acquisition of step 1 Learning Samples includes gathering taxi on road, bus, minibus, car and non- The picture of motor vehicles.
In step 2 convolutional neural networks model structure particularly as follows:
A: determine the number of plies of convolutional neural networks: convolutional neural networks totally 8 layers, front 5 layers for be used for carrying out convolution and under adopt The convolutional layer of sample, latter 3 layers is full articulamentum, and the output of a last full articulamentum is the svm classifier with 5 outputs Device；
B: determine ground floor neural network structure: in each picture input to neural network structure, be all scaled to 227* The picture of 227 sizes, and three the color dimension inputs of point red, green, blue；At ground floor, 96 convolution filters, each filter are set The size of ripple device is 11*11, and convolution step-length is 4, is 55*55 through calculating the characteristic pattern size of the 1st layer of output, characteristic pattern Number be 96；
In order to improve training speed, at a convolutional layer down-sampling layer disposed behind, during down-sampling, select ReLu Activation primitive:
F (t)=max (0, t) (1)
Wherein t is the feature of input；
Through the feature extraction of ground floor, the characteristic pattern size drawn is 27*27；
C: the number of second layer convolution filter is set to 256, and characteristic pattern input size is the 27*27 that b obtains；Second Layer output characteristic figure size after convolution and down-sampling are trained is 13*13；Hereafter, using each layer output as next layer Input, wherein, the convolution filter number of third layer, the 4th layer and layer 5 is respectively set to 384,384 and 256, The characteristic pattern size finally drawn after 5 layers of training is 6*6；
D: obtain characteristic vector: according to the result of c output, the node number calculating the output of the first two full articulamentum is 6*6* 256=4096；The size obtained by c is that 6*6 characteristic pattern is arranged as a column vector, obtains final characteristic vector；
E: output result: the characteristic vector obtained by d is sent to the SVM of last layer and classifies.
In step 3 with liblinear grader build support vector cassification model particularly as follows:
1.: determine mode classification: using 1-V-1 mode is exactly i.e. together into many points by many 2 classifiers combination Class device；
2.: SVM uses kernel function RBF；
3.: parameter c and γ to liblinear-2.1 grader are done preferably
Grid.py file is used automatically to do preferably to parameter penalty factor c and nuclear parameter γ, last of terminal demonstration Row output (c, γ, cross validation precision), for optimum result.
The invention has the beneficial effects as follows: different from the algorithm relying on priori extraction feature in traditional method, the present invention Convolutional neural networks can under training data drives adaptively construction feature describe, there is higher motility and pervasive Property.The convolutional neural networks of the present invention can be by original image directly as input, it is to avoid number extra in tional identification algorithm Data preprocess process.Extract feature by the convolutional neural networks of the present invention and can solve over-fitting, utilize the GPU speed-up computation can Improve the training time.And by the feature of the network extraction of deep layer it can be avoided that by illumination, the impact of color etc., and can carry Take abundanter, more abstract characteristics of image.The present invention uses convolutional neural networks and support vector machine to combine, and both can carry Take abundant feature, suitable grader can be chosen according to the feature of oneself data set again thus improve the standard of whole disaggregated model Really property.The present invention utilizes the full articulamentum output of convolutional neural networks as the character representation of vehicle image, then utilizes SVM to divide It is classified by class device, thus obtains preferable vehicle classification accuracy rate.
Accompanying drawing explanation
Fig. 1 is the structural representation of convolutional neural networks in a kind of vehicle classification method based on convolutional neural networks of the present invention Figure；
Fig. 2 is the thinking figure of 1-V-1 mode in a kind of vehicle classification method based on convolutional neural networks of the present invention；
Fig. 3 is the flow chart of a kind of vehicle classification method based on convolutional neural networks of the present invention.
Detailed description of the invention
The present invention is described in detail with detailed description of the invention below in conjunction with the accompanying drawings.
A kind of vehicle classification method based on convolutional neural networks of the present invention, under computer Ubuntu system, ratio exists Under windows system, speed is fast, and therefore the present invention illustrates as a example by under Ubuntu system.
Preparation:
Caffe degree of depth learning framework platform is built under computer Ubuntu system.
Build convolutional neural networks model, as shown in Figure 1:
A: determine the number of plies of convolutional neural networks: convolutional neural networks totally 8 layers, front 5 layers for be used for carrying out convolution and under adopt The convolutional layer (convolutional) of sample, latter 3 layers is full articulamentum (full-connected), a last full articulamentum Output output be have 5 output SVM (Support Vector Machine, support vector machine) graders；
B: determine ground floor neural network structure: in each picture input to neural network structure, be all scaled to 227* The picture of 227 sizes, and three the color dimension inputs of point red, green, blue；At ground floor, 96 convolution filters, each filter are set The size of ripple device is 11*11, and convolution step-length is 4, is 55*55 through calculating the characteristic pattern size of the 1st layer of output, characteristic pattern Number be 96；
In order to improve training speed, at a convolutional layer down-sampling layer disposed behind, during down-sampling, select ReLu Activation primitive:
F (t)=max (0, t) (1)
Wherein t is the feature of input；
Through the feature extraction of ground floor, the characteristic pattern size drawn is 27*27；
C: the number of second layer convolution filter is set to 256, and characteristic pattern input size is the 27*27 that b obtains；Second Layer output characteristic figure size after convolution and down-sampling are trained is 13*13；Hereafter, using each layer output as next layer Input, wherein, the convolution filter number of third layer, the 4th layer and layer 5 is respectively set to 384,384 and 256, The characteristic pattern size finally drawn after 5 layers of training is 6*6；
D: obtain characteristic vector: according to the result of c output, the node number calculating the output of the first two full articulamentum is 6*6* 256=4096；The size obtained by c is that 6*6 characteristic pattern is arranged as a column vector, obtains final characteristic vector；
E: output result: the characteristic vector obtained by d is sent to the SVM of last layer and classifies.
Build support vector cassification model
Selecting the liblinear-2.1 grader increased income, arranging parameter c is 100 (through test of many times, when c is 100 Accuracy rate the highest)
1.: determine mode classification: as in figure 2 it is shown, using 1-V-1 mode is exactly i.e. to one by many 2 classifiers combination Rise and become multi-categorizer；
2.: SVM uses kernel function RBF (Radial Basis Function, RBF)；
3.: parameter c and γ to liblinear-2.1 grader are done preferably
Grid.py file is used automatically to do preferably to parameter penalty factor c and nuclear parameter γ, last of terminal demonstration Row output (c, γ, cross validation precision), for optimum result.
A kind of vehicle classification method based on convolutional neural networks of the present invention, flow process is as it is shown on figure 3, specifically according to following step Rapid enforcement:
Step 1: obtain learning sample, including gathering taxi, bus, minibus, car and non-machine on road The picture of motor-car totally 5 classes, every class collection 300～400 pictures are as training set, and additionally every class gathers 150 pictures conducts again Test set, picture size, form are not fixed, and are stamped class label to sample, and then sample is converted into what caffe identified The formatted data of lmdb.
Step 2: learning sample step 1 got is as training data, training convolutional neural networks CNN (Convolutional Neural Network) model, adjusts in solver.prototxt and train_val.prototxt The numerical value in face thus obtain outstanding network model's parameter.
Step 3: use the convolutional neural networks model trained in step 2, extracts the feature of training data, uses The support vector cassification model that liblinear grader builds carries out ten folding cross-trainings, obtains support vector cassification mould Type；
Step 4: use the feature of the auto model that convolutional neural networks model extraction is to be sorted in step 2, then use The support vector cassification model that step 3 obtains, obtains the class of vehicle belonging to vehicle image to be sorted.
The convolutional neural networks of the present invention can be by original image directly as input, it is to avoid extra in tional identification algorithm Process of data preprocessing.Extract feature by the convolutional neural networks of the present invention and can solve over-fitting, utilize GPU accelerometer Calculation can improve the training time.And by the feature of the network extraction of deep layer it can be avoided that by illumination, the impact of color etc., and energy The characteristics of image that enough extractions are abundanter, more abstract.The present invention uses convolutional neural networks and support vector machine to combine, and both may be used To extract abundant feature, suitable grader can be chosen according to the feature of oneself data set again thus improve whole disaggregated model Accuracy.The present invention utilizes the full articulamentum output of convolutional neural networks as the character representation of vehicle image, then utilizes It is classified by SVM classifier, thus obtains preferable vehicle classification accuracy rate.

Claims (4)

1. a vehicle classification method based on convolutional neural networks, it is characterised in that specifically implement according to following steps:
Step 1: obtain learning sample, and stamp class label to sample；
Step 2: learning sample step 1 got, as training data, training convolutional neural networks model, obtains outstanding Network model's parameter；
Step 3: use the convolutional neural networks model trained in step 2, extracts the feature of training data, uses The support vector cassification model that liblinear grader builds carries out ten folding cross-trainings, obtains support vector cassification mould Type；
Step 4: use the feature of the auto model that convolutional neural networks model extraction is to be sorted in step 2, then use step 3 The support vector cassification model obtained, obtains the class of vehicle belonging to vehicle image to be sorted.
A kind of vehicle classification method based on convolutional neural networks the most according to claim 1, it is characterised in that described step The acquisition of rapid 1 Learning Samples includes gathering taxi, bus, minibus, car and the figure of bicycle on road Sheet.
A kind of vehicle classification method based on convolutional neural networks the most according to claim 1, it is characterised in that described step In rapid 2 convolutional neural networks model structure particularly as follows:
A: determine the number of plies of convolutional neural networks: convolutional neural networks totally 8 layers, first 5 layers is to be used for carrying out convolution and down-sampling Convolutional layer, latter 3 layers is full articulamentum, and the output of a last full articulamentum is the SVM classifier with 5 outputs；
B: determine ground floor neural network structure: in each picture input to neural network structure, be all scaled to 227*227 big Little picture, and three the color dimension inputs of point red, green, blue；At ground floor, 96 convolution filters are set, each wave filter Size is 11*11, and convolution step-length is 4, is 55*55 through calculating the characteristic pattern size of the 1st layer of output, the number of characteristic pattern It it is 96；
In order to improve training speed, at a convolutional layer down-sampling layer disposed behind, ReLu is selected to activate during down-sampling Function:
F (t)=max (0, t) (1)
Wherein t is the feature of input；
Through the feature extraction of ground floor, the characteristic pattern size drawn is 27*27；
C: the number of second layer convolution filter is set to 256, and characteristic pattern input size is the 27*27 that b obtains；Second layer warp Cross convolution and down-sampling training after output characteristic figure size be 13*13；Hereafter, using defeated as next layer of output of each layer Entering, wherein, the convolution filter number of third layer, the 4th layer and layer 5 is respectively set to 384,384 and 256, warp The characteristic pattern size finally drawn after crossing 5 layers of training is 6*6；
D: obtain characteristic vector: according to the result of c output, the node number calculating the output of the first two full articulamentum is 6*6*256 =4096；The size obtained by c is that 6*6 characteristic pattern is arranged as a column vector, obtains final characteristic vector；
E: output result: the characteristic vector obtained by d is sent to the SVM of last layer and classifies.
A kind of vehicle classification method based on convolutional neural networks the most according to claim 1, it is characterised in that described step In rapid 3 with liblinear grader build support vector cassification model particularly as follows:
1.: determine mode classification: using 1-V-1 mode is exactly i.e. together into multi-categorizer by many 2 classifiers combination；
2.: SVM uses kernel function RBF；
3.: parameter c and γ to liblinear-2.1 grader are done preferably
Using grid.py file to be automatically parameter penalty factor c and nuclear parameter γ preferably, last column of terminal demonstration is defeated Go out (c, γ, cross validation precision), for optimum result.
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Application publication date: 20161207