CN105354568A - Convolutional neural network based vehicle logo identification method - Google Patents

Convolutional neural network based vehicle logo identification method Download PDF

Info

Publication number
CN105354568A
CN105354568A CN201510523632.0A CN201510523632A CN105354568A CN 105354568 A CN105354568 A CN 105354568A CN 201510523632 A CN201510523632 A CN 201510523632A CN 105354568 A CN105354568 A CN 105354568A
Authority
CN
China
Prior art keywords
convolutional neural
neural networks
car
mark
layer
Prior art date
Application number
CN201510523632.0A
Other languages
Chinese (zh)
Inventor
韩红
焦李成
张鼎
王伟
叶旭庆
李阳阳
马文萍
王爽
Original Assignee
西安电子科技大学
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 西安电子科技大学 filed Critical 西安电子科技大学
Priority to CN201510523632.0A priority Critical patent/CN105354568A/en
Publication of CN105354568A publication Critical patent/CN105354568A/en

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/20Image acquisition
    • G06K9/32Aligning or centering of the image pick-up or image-field
    • G06K9/3216Aligning or centering of the image pick-up or image-field by locating a pattern
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods
    • G06N3/084Back-propagation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K2209/15Detection and recognition of car license plates

Abstract

The invention provides a convolutional neural network based vehicle logo identification method. The method is specifically implemented by the following steps of: (1) inputting a to-be-detected picture shot by a high-resolution camera device in a traffic intersection; (2) positioning a vehicle logo; (3) constructing and training a convolutional neural network; and (4) identifying the vehicle logo. With the adoption of the convolutional neural network (CNN) based vehicle logo identification method, the shortcomings of complicated extraction feature operator, poor timeliness and complicated model in the prior art can be effectively overcome, and the calculation amount is effectively reduced; and features of CNN self-learning have higher robustness on environmental change, so that the identification rate of the vehicle logo is increased.

Description

Based on the automobile logo identification method of convolutional neural networks

Technical field

The invention belongs to technical field of image processing, further relate to a kind of automobile logo identification method based on convolutional neural networks of the mode identification technology based on image.The present invention is directed in traffic system, the vehicle pictures of the high definition photographing device gained arranged by crossing, carries out the vehicle-logo location of automobile, and then identifies the car mark of automobile, realizes locating the robotization of car target and identifying.

Background technology

Along with improving constantly of social economy's level and popularizing of vehicle, the demand of the communication that scale constantly expands to more intelligentized technology and system is larger, and intelligent transportation system has become the hot issue of social life.Vehicle identification system is as the important component part of intelligent transportation system, and in expressway access, parking lot unattended, the vehicles peccancy field such as automatically to record has this to apply widely, and its realization has very large economic worth and realistic meaning.

Vehicle-logo recognition is an importance of vehicle identification.Vehicle-logo recognition technology refers to digital picture or video signal flow for object, by image procossing and automatic identifying method, obtains a kind of practical technique of motor vehicles brand message.Vehicle-logo recognition system comprises car target location and vehicle-logo recognition binomial gordian technique.The features such as the otherness under the diversity had due to car sample body and varying environment condition, add car target locational uncertainty in the pictorial information that artificial shooting obtains, therefore find a kind of outstanding vehicle-logo location and recognition methods multi-crossed disciplines and challenging technical matters.

The method of existing vehicle-logo location, mostly adopt the method for rim detection and grey level histogram template matches, because car mark is little, these class methods are easily subject to the impact of background environment.The method of some vehicle-logo recognition is suggested, particularly use the more recognition methods based on histograms of oriented gradients HOG characteristic sum support vector machines sorter at present, major part is all based on car plate and car target relative position determination car cursor position, then extract car target histograms of oriented gradients HOG feature, utilize support vector machines to be trained to sorter and carry out vehicle-logo recognition.In vehicle-logo recognition, histograms of oriented gradients HOG adds support vector machines algorithm owing to have employed histograms of oriented gradients HOG feature, and histograms of oriented gradients HOG descriptor generative process is tediously long, cause speed slow, poor real, due to the character of gradient, this descriptor is quite responsive to noise.Existing most of vehicle-logo recognition algorithm, process is complicated, and calculated amount is too large, and discrimination is not high, is easily subject to the impact of environmental baseline, so need the proposition of new research method.

In recent years, along with the development of large data, degree of depth Learning Studies, convolutional neural networks CNN has become the study hotspot of current speech analysis and field of image recognition, its weights shared network structure makes it more to be similar to biological neural network, reduce the complexity of network model, decrease the quantity of weights.It is more obvious that this advantage shows when the input of network is multidimensional image, makes image directly as the input of network, can avoid feature extraction complicated in tional identification algorithm and data reconstruction processes.Convolutional network is a multilayer perceptron for identifying two-dimensional shapes and particular design, and the distortion of this network structure to translation, proportional zoom, inclination or his form altogether has height unchangeability.

D.F.Llorca, R.Arroyo, the method of a set of vehicle-logo recognition based on histograms of oriented gradients HOG and support vector machines is proposed in paper " VehiclelogorecognitionintrafficimagesusingHOGfeaturesand SVM " (Proceedingsofthe16thInternationalIEEEAnnualConferenceonI ntelligentTransportationSystems, 2013) that M.A.Sotelo delivers at it.The method is License Plate first, car mark is utilized to be in priori directly over car plate, above car plate, use moving window to shift to an earlier date candidate target region, then extract candidate region histograms of oriented gradients HOG feature, the sorter finally utilizing support vector machines to train carries out the classification of car mark.The weak point that the method exists is, one, and owing to the process employs histograms of oriented gradients HOG feature, histograms of oriented gradients HOG descriptor generative process is tediously long, causes speed slow, poor real.Its two, due to the character of the method gradient, histograms of oriented gradients HOG descriptor is quite responsive to noise, is easily subject to the interference of noise.

Propose a kind of car mark based on pattern-recognition in the patent " a kind of car mark based on pattern-recognition is located and recognition methods automatically " (number of patent application: CN201410367377, publication number: CN104182728A) of Pci-Suntek Technology Co., Ltd.'s application automatically to locate and recognition methods.First the method utilizes car plate detection technique, obtain size and the position of car plate, thus according to car plate and car target relative position, carry out car target just to locate, secondly the strong classifier Adaboost algorithm based on Ha Er Haar feature is utilized to carry out car target secondary location, obtain some doubtful car target areas, the support vector machines algorithm based on histograms of oriented gradients HOG feature is again utilized to screen doubtful car mark region, choose there is maximum confidence region as vehicle-logo location result, the support vector machines algorithm based on HOG feature is finally utilized to carry out the identification of car target.The weak point that the method exists is, have employed based on the strong classifier Adaboost algorithm of Ha Er Haar feature and the support vector machines algorithm based on histograms of oriented gradients HOG feature in positioning flow, the support vector machines algorithm based on histograms of oriented gradients HOG feature is have employed in vehicle-logo recognition flow process, altogether have employed three sorters, considerably increase computation complexity.And HOG descriptor generative process length consuming time, cause speed slow, poor real.

Patent " car mark automatic identifying method and the system " (number of patent application: CN201310170528 of Shanghai Communications University's application, publication number: CN103279738A) a kind of car mark automatic identifying method of middle proposition, comprise off-line training subsystem and ONLINE RECOGNITION subsystem.Intensive Scale invariant features transform dense-SIFT, according to the correlativity of intensive Scale invariant features transform dense-SIFT and visual word, is mapped to all visual word and represents by the method, increases feature interpretation.Adopt support vector machine training cart mark sorter, realize vehicle-logo recognition.The weak point that the method exists is that, owing to have employed intensive Scale invariant features transform dense-SIFT feature operator, dimension is high, and computing time is long, poor real.

Summary of the invention

The object of the invention is the deficiency existed for above-mentioned prior art, propose a kind of automobile logo identification method based on convolutional neural networks.The present invention's calculated amount compared with other vehicle-logo recognition technology in prior art is little, and accuracy is high, strong adaptability.

The concrete steps that the present invention realizes comprise as follows:

1., based on an automobile logo identification method for convolutional neural networks, comprise the steps:

(1) the car mark picture to be detected that in traffic intersection, high definition photographing device obtains is inputted;

(2) vehicle-logo location:

(2a) binaryzation operation is carried out to the car mark picture to be detected of input, obtain the car after binaryzation and to mark on a map sheet;

(2b) car after binaryzation is marked on a map the morphological operation that sheet corrodes and expand, obtain UNICOM region;

(2c) in UNICOM region, utilize the ratio of width to height and the rectangular characteristic in car plate UNICOM region, filter out car plate UNICOM region, obtain top-left coordinates (x1, y1) and the lower right coordinate (x2, y2) in car plate UNICOM region;

(2d) mark on a map in sheet at measuring car to be checked, intercept car mark areal map by sliding window, sliding window base is initial with car plate upper edge, and up sliding and intercept candidate region 3 times in the Central Line along car plate, obtains car mark areal map;

(3) also training convolutional neural networks CNN is built:

(3a) build the convolutional neural networks CNN containing 7 layers, 7 layers is convolutional layer Conv1 successively, pond layer Pool2, convolutional layer Conv3, pond layer Pool4, full articulamentum Fc5, full articulamentum Fc6, classification layer Softmax7;

(3b) input has marked and the car mark area sample picture of gray processing, and training convolutional neural networks CNN, until the loss function J (θ)≤0.0001 of output layer, obtains the convolutional neural networks CNN of vehicle-logo recognition;

(4) vehicle-logo recognition:

(4a) gray processing operation is carried out to car mark areal map;

(4b) the car mark areal map resolution after gray processing is normalized to 38 × 38 pixel sizes, obtains the car after processing and mark on a map;

(4c) by process after car mark on a map input vehicle-logo recognition convolutional neural networks CNN, final Output rusults.

The present invention compared with prior art has the following advantages:

The first, because the present invention adopts first positioning licence plate region, then the vehicle-logo location method of region with sliding window intercepting car mark region is just being gone up at car plate, overcome extracting directly car mark in prior art and be subject to background environment impact, can not, by the problem accurately extracted, make the present invention can extract car mark region exactly from complex background environment.

The second, because the present invention adopts the automobile logo identification method based on convolutional neural networks CNN, by the network self study feature of multilayer in convolutional neural networks CNN, avoid the process needing engineer's feature in tional identification algorithm, and the feature of convolutional neural networks CNN self study has higher robustness to environmental change, make to present invention reduces calculated amount and complicacy, improve discrimination, to complex background, there is stronger adaptability.

Accompanying drawing explanation

Fig. 1 is process flow diagram of the present invention;

Fig. 2 is the schematic diagram of vehicle-logo location of the present invention;

Fig. 3 is convolutional neural networks CNN structural drawing of the present invention;

Fig. 4 is that the car of portion markings of the present invention is marked on a map.

Embodiment

Below in conjunction with accompanying drawing, the present invention will be further described.

With reference to Fig. 1, the concrete steps that the present invention realizes are as follows:

Step 1, the picture to be detected that in input traffic intersection, high definition photographing device takes.

Picture to be detected comprises apparent mark car plate and car mark, and standard car plate area size is 180 × 60 pixels.

Step 2, vehicle-logo location.

Carry out binaryzation operation to the picture to be detected of input, obtain the picture after binaryzation, concrete operations are:

The first step, chooses the turquoise rgb value of primary colors red of car plate background color in 50 marker samples, the average of the turquoise rgb value of statistics primary colors red;

Second step, according to the following formula, carries out binaryzation operation to the car mark picture to be detected of input:

Wherein, q ijrepresent the gray-scale value of the pixel of the picture after binaryzation, r 0, g 0, b 0represent the turquoise average of primary colors red of samples pictures pixel respectively, r ij,g ij, b ijrepresent the turquoise value of primary colors red of picture pixels to be detected point respectively, i, j represent line number and the columns of picture pixels point respectively.

To the morphological operation that the picture after binaryzation corrodes and expands, obtain UNICOM region.In UNICOM region, utilize the ratio of width to height in car plate UNICOM region and the ratio of width to height of rectangular characteristic and car plate to be 3:1, car plate is wider than height, and be less than 5 degree with the horizontal pitch angle of shooting road, filter out car plate UNICOM region as Fig. 2 (a), obtain the top-left coordinates (x in car plate UNICOM region 1,y 1) and lower right coordinate (x 2,y 2).

In picture to be detected, intercept car mark areal map with sliding window, the window of sliding window is square, and its length of side is step-length is wherein, l represents the length of side of sliding window window, and h represents the step-length of sliding window window, (x 1,y 1) represent the top-left coordinates in car plate UNICOM region, (x 2,y 2) represent the lower right coordinate in car plate UNICOM region.Sliding window is initial with car plate upper edge below, and up slide and intercept candidate region 3 times in the Central Line along car plate, as shown in Fig. 2 (b), the final car mark areal map exported is as Fig. 2 (c).

Step 3, builds and training convolutional neural networks.

Build the convolutional neural networks CNN containing 7 layers as shown in Figure 3,7 layers is convolutional layer Conv1 successively, pond layer Pool2, convolutional layer Conv3, pond layer Pool4, full articulamentum Fc5, full articulamentum Fc6, classification layer Softmax7, the convolutional neural networks CNN concrete steps built containing the vehicle-logo recognition of 7 layers are as follows:

1st step, by the car mark areal map of 38 × 38 pixel sizes input convolutional layer Conv1, to it, to carry out block size be 5 × 5 pixels and step-length is the convolution operation of 1 pixel, altogether uses 32 convolution kernels, obtains the characteristic pattern of 32 34 × 34 pixel sizes;

32 characteristic patterns that convolutional layer Conv1 exports are input to pond layer Pool2 by the 2nd step, and carry out the operation of maximum pondization to it, the size of pond block is 2 × 2 pixels, and step-length is 1 pixel, obtains the characteristic pattern that 32 resolution are 17 × 17 pixels;

3rd step, 32 characteristic patterns input convolutional layer Conv3 that pond layer Pool2 is exported, to it, to carry out block size be 5 × 5 pixels and step-length is the convolution operation of 1 pixel, altogether uses 64 convolution kernels, obtains the characteristic pattern that 64 resolution are 13 × 13 pixels;

4th step, 64 characteristic pattern input pond layer Pool4 that convolutional layer Conv3 is exported, carry out the operation of maximum pondization to it, the size of pond block is 2 × 2 pixels, and step-length is 1 pixel, obtains the characteristic pattern that 64 resolution are 7 × 7 pixels;

5th step, 64 characteristic patterns that pond layer Pool4 exports are inputted full articulamentum Fc5, according to the following formula, wherein each pixel is activated, obtain the value of the pixel of the characteristic pattern after activating, characteristic pattern after activating is arranged in 1 dimensional vector with the order of row, obtains the proper vector of 1 × 3136 dimension:

f ( x ) = e x - e - x e x + e - x

Wherein, f (x) represents the value of the pixel of the characteristic pattern after activating, and x represents the value of the pixel activating front characteristic pattern, and e represents a natural constant infinitely do not circulated, and value is 2.7182;

6th step, inputs full articulamentum Fc6 by the proper vector that full articulamentum Fc5 exports, forms general neural network, and output is the proper vector of 1 × 500 dimension;

7th step, the proper vector input classification layer Softmax7 that full articulamentum Fc6 is exported, obtain the tag along sort of car mark areal map, this layer of meeting calculates the probability of often kind of tag along sort, and the label of maximum probability is exported, wherein the expectation function of softmax classification is as follows:

h θ ( α ( i ) ) = p ( β ( i ) = 1 | α ( i ) ; θ ) p ( β ( i ) = 2 | α ( i ) ; θ ) . . . p ( β ( i ) = k | α ( i ) ; θ )

Wherein, h θ(α) expectation function that softmax classifies is represented, α represents the proper vector that in convolutional neural networks CNN, full articulamentum Fc6 exports, β represents the label corresponding with the proper vector α that articulamentum Fc6 complete in convolutional neural networks CNN exports, p (β=t| α; When θ) representing the proper vector α being input as full articulamentum Fc6 output in convolutional neural networks CNN, label β equals the probability of t, t ∈ 1,2 ..., k, θ represent model parameter and θ 1, θ 2..., θ k∈ R n+1, softmax Classification Loss function is as follows:

J ( θ ) = - 1 m [ Σ i = 1 m β ( i ) logh θ ( α ( i ) ) + ( 1 - β ( i ) ) l o g ( 1 - h θ ( α ( i ) ) ) ]

Wherein, J (θ) represents loss function, and m represents the quantity of car mark areal map sample, h θ(α) expectation function that softmax classifies is represented, α represents the proper vector that in convolutional neural networks CNN, full articulamentum Fc6 exports, β represents the label corresponding with the proper vector α that articulamentum Fc6 complete in convolutional neural networks CNN exports, and θ represents model parameter.

Input has marked and the car mark area sample picture of gray processing, training convolutional neural networks CNN, and the process of training is as follows:

The first step, in the forward direction stage, gets a sample from sample set, and sample is inputted convolutional neural networks CNN and calculate corresponding actual output, in this stage, information through conversion step by step, is sent to output layer from input layer;

Second step, in the back-propagation stage, calculates the actual difference exporting the ideal corresponding with sample label and export, by the method for minimization error, and backpropagation adjustment weight matrix;

3rd step, repeats the operation of the first step and second step, and the loss function J (θ)≤0.0001 of the output until convolutional neural networks CNN classifies after layer Softmax7, obtains the convolutional neural networks CNN of vehicle-logo recognition.

Step 4, vehicle-logo recognition.

To car mark areal map, carry out gray processing operation, and car mark areal map resolution is contracted to 38 × 38 pixel sizes, obtain the car after processing and mark on a map, as shown in Figure 4.Car after process is marked on a map and inputs the convolutional neural networks CNN of vehicle-logo recognition, final Output rusults.

Below in conjunction with emulation experiment, effect of the present invention is further described.

1, emulation experiment condition:

The present invention's database used be collect and make one group comprise 10 class car target vehicle mark bases, wherein each car indicates 3600 training plans and 240 test patterns, totally 36000 training plans and 2400 test patterns, negative sample is 120,000, comprises various non-car target picture.Hardware platform is: IntelCore2DuoCPUE65502.33GHZ, 3GBRAM, software platform: vs2010, MATLABR2012a.

2, experiment content and result:

The ten kind car marks of the present invention first in collecting cart mark database, totally 36000 training datas and 2400 test datas.By License Plate, car mark is just located accurately to locate with car mark and is finally accurately determined car target area, is input in the convolutional neural networks CNN sorter trained by train mark areal map and classifies, obtain a result.The simulation result of contrast following table, in 2400 data of test, each classification Audi, Honda, BYD, mark, Buick, popular, Toyota, Jeep, Kia, Chang'an, number of errors is 6 respectively, 10,12,5,6,8,11,7,8,9.Can find out that the present invention has higher discrimination at identification car timestamp.

Car mark class Audi Honda BYD Mark Buick Error number 6 10 12 5 6 Car mark class Popular Toyota Jeep Kia Chang'an Error number 8 11 7 8 9

Car mark class Audi Honda BYD Mark Buick Discrimination 97.5% 95.83% 95% 97.9% 97.5% Car mark class Popular Toyota Jeep Kia Chang'an Discrimination 96.67% 95.42% 97.18% 96.67% 96.25%

Claims (7)

1., based on an automobile logo identification method for convolutional neural networks, comprise the steps:
(1) the car mark picture to be detected that in traffic intersection, high definition photographing device obtains is inputted;
(2) vehicle-logo location:
(2a) binaryzation operation is carried out to the car mark picture to be detected of input, obtain the car after binaryzation and to mark on a map sheet;
(2b) car after binaryzation is marked on a map the morphological operation that sheet corrodes and expand, obtain UNICOM region;
(2c) in UNICOM region, utilize the ratio of width to height and the rectangular characteristic in car plate UNICOM region, filter out car plate UNICOM region, obtain the top-left coordinates (x in car plate UNICOM region 1,y 1) and lower right coordinate (x 2,y 2);
(2d) mark on a map in sheet at measuring car to be checked, intercept car mark areal map by sliding window, sliding window base is initial with car plate upper edge, and up sliding and intercept candidate region 3 times in the Central Line along car plate, obtains car mark areal map;
(3) also training convolutional neural networks CNN is built:
(3a) build the convolutional neural networks CNN containing 7 layers, 7 layers is convolutional layer Conv1 successively, pond layer Pool2, convolutional layer Conv3, pond layer Pool4, full articulamentum Fc5, full articulamentum Fc6, classification layer Softmax7;
(3b) input has marked and the car mark area sample picture of gray processing, and training convolutional neural networks CNN, until the loss function J (θ)≤0.0001 of output layer, obtains the convolutional neural networks CNN of vehicle-logo recognition;
(4) vehicle-logo recognition:
(4a) gray processing operation is carried out to car mark areal map;
(4b) the car mark areal map resolution after gray processing is normalized to 38 × 38 pixel sizes, obtains the car after processing and mark on a map;
(4c) by process after car mark on a map input vehicle-logo recognition convolutional neural networks CNN, final Output rusults.
2. the automobile logo identification method based on convolutional neural networks according to claim 1, it is characterized in that: the car mark picture to be detected described in step (1) comprises apparent mark car plate and car mark, and standard car plate area size is 180 × 60 pixels.
3. the automobile logo identification method based on convolutional neural networks according to claim 1, is characterized in that: described in step (2a) to carry out the step of binaryzation operation to the car mark picture to be detected of input as follows:
The first step, chooses the turquoise rgb value of primary colors red of car plate background color in 50 marker samples, the average of the turquoise rgb value of statistics primary colors red;
Second step, according to the following formula, carries out binaryzation operation to the car mark picture to be detected of input:
Wherein, q ijrepresent the gray-scale value of the pixel of the picture after binaryzation, r 0, g 0, b 0represent the turquoise average of primary colors red of samples pictures pixel respectively, r ij,g ij, b ijrepresent the turquoise value of primary colors red of picture pixels to be detected point respectively, i, j represent line number and the columns of picture pixels point respectively.
4. the automobile logo identification method based on convolutional neural networks according to claim 1, is characterized in that:
The ratio of width to height and the rectangular characteristic in the car plate UNICOM region described in step (2c) refer to, the ratio of width to height of car plate is 3:1, and car plate is wider than height, and are less than 5 degree with the horizontal pitch angle of shooting road.
5. the automobile logo identification method based on convolutional neural networks according to claim 1, is characterized in that: the window of the sliding window described in step (2d) is square, and its length of side is: step-length is: wherein, l represents the length of side of sliding window window, and h represents the step-length of sliding window window, (x 1,y 1) represent the top-left coordinates in car plate UNICOM region, (x 2,y 2) represent the lower right coordinate in car plate UNICOM region.
6. the automobile logo identification method based on convolutional neural networks according to claim 1, is characterized in that: the concrete steps that step (3a) described structure contains the convolutional neural networks CNN of 7 layers are as follows:
1st step, by the car mark areal map of 38 × 38 pixel sizes input convolutional layer Conv1, to it, to carry out block size be 5 × 5 pixels and step-length is the convolution operation of 1 pixel, altogether uses 32 convolution kernels, obtains the characteristic pattern of 32 34 × 34 pixel sizes;
32 characteristic patterns that convolutional layer Conv1 exports are input to pond layer Pool2 by the 2nd step, and carry out the operation of maximum pondization to it, the size of pond block is 2 × 2 pixels, and step-length is 1 pixel, obtains the characteristic pattern that 32 resolution are 17 × 17 pixels;
3rd step, 32 characteristic patterns input convolutional layer Conv3 that pond layer Pool2 is exported, to it, to carry out block size be 5 × 5 pixels and step-length is the convolution operation of 1 pixel, altogether uses 64 convolution kernels, obtains the characteristic pattern that 64 resolution are 13 × 13 pixels;
4th step, 64 characteristic pattern input pond layer Pool4 that convolutional layer Conv3 is exported, carry out the operation of maximum pondization to it, the size of pond block is 2 × 2 pixels, and step-length is 1 pixel, obtains the characteristic pattern that 64 resolution are 7 × 7 pixels;
5th step, 64 characteristic patterns that pond layer Pool4 exports are inputted full articulamentum Fc5, according to the following formula, wherein each pixel is activated, obtain the value of the pixel of the characteristic pattern after activating, characteristic pattern after activating is arranged in 1 dimensional vector with the order of row, obtains the proper vector of 1 × 3136 dimension:
f ( x ) = e x - e - x e x + e - x
Wherein, f (x) represents the value of the pixel of the characteristic pattern after activating, and x represents the value of the pixel activating front characteristic pattern, and e represents a natural constant infinitely do not circulated, and value is 2.7182;
6th step, inputs full articulamentum Fc6 by the proper vector that full articulamentum Fc5 exports, forms general neural network, and output is the proper vector of 1 × 500 dimension;
7th step, the proper vector input classification layer Softmax7 that full articulamentum Fc6 is exported, obtain the tag along sort of car mark areal map, this layer of meeting calculates the probability of often kind of tag along sort, and the label of maximum probability is exported, wherein the expectation function of softmax classification is as follows:
h θ ( α ( i ) ) = p ( β ( i ) = 1 | α ( i ) ; θ ) p ( β ( i ) = 2 | α ( i ) ; θ ) . . . p ( β ( i ) = k | α ( i ) ; θ )
Wherein, h θ(α) expectation function that softmax classifies is represented, α represents the proper vector that in convolutional neural networks CNN, full articulamentum Fc6 exports, β represents the label corresponding with the proper vector α that articulamentum Fc6 complete in convolutional neural networks CNN exports, p (β=t| α; When θ) representing the proper vector α being input as full articulamentum Fc6 output in convolutional neural networks CNN, label β equals the probability of t, t ∈ 1,2 ..., k, θ represent model parameter and θ 1, θ 2..., θ k∈ R n+1, softmax Classification Loss function is as follows:
J ( θ ) = - 1 m [ Σ i = 1 m β ( i ) logh θ ( α ( i ) ) + ( 1 - β ( i ) ) l o g ( 1 - h θ ( α ( i ) ) ) ]
Wherein, J (θ) represents loss function, and m represents the quantity of car mark areal map sample, h θ(α) expectation function that softmax classifies is represented, α represents the proper vector that in convolutional neural networks CNN, full articulamentum Fc6 exports, β represents the label corresponding with the proper vector α that articulamentum Fc6 complete in convolutional neural networks CNN exports, and θ represents model parameter.
7. the automobile logo identification method based on convolutional neural networks according to claim 1, is characterized in that: the concrete steps of step (3b) described training convolutional neural networks CNN are as follows:
1st step, in the forward direction stage, gets a sample from sample set, sample is inputted convolutional neural networks CNN and calculate corresponding actual output, in this stage, information through conversion step by step, is sent to convolutional neural networks CNN output layer from convolutional neural networks CNN input layer;
2nd step, in the back-propagation stage, calculates the actual difference exporting the ideal corresponding with sample label and export of convolutional neural networks CNN, by the method for minimization error, and the weights of backpropagation adjustment convolutional neural networks CNN;
3rd step, repeats the operation of the 1st step and the 2nd step, the loss function J (θ)≤0.0001 of the output until convolutional neural networks CNN classifies after layer Softmax7.
CN201510523632.0A 2015-08-24 2015-08-24 Convolutional neural network based vehicle logo identification method CN105354568A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201510523632.0A CN105354568A (en) 2015-08-24 2015-08-24 Convolutional neural network based vehicle logo identification method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201510523632.0A CN105354568A (en) 2015-08-24 2015-08-24 Convolutional neural network based vehicle logo identification method

Publications (1)

Publication Number Publication Date
CN105354568A true CN105354568A (en) 2016-02-24

Family

ID=55330535

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201510523632.0A CN105354568A (en) 2015-08-24 2015-08-24 Convolutional neural network based vehicle logo identification method

Country Status (1)

Country Link
CN (1) CN105354568A (en)

Cited By (30)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105678344A (en) * 2016-02-29 2016-06-15 浙江群力电气有限公司 Intelligent classification method for power instrument equipment
CN105868785A (en) * 2016-03-30 2016-08-17 乐视控股(北京)有限公司 Image identification method based on convolutional neural network and image identification system thereof
CN105957238A (en) * 2016-05-20 2016-09-21 聚龙股份有限公司 Banknote management method and system
CN105956524A (en) * 2016-04-22 2016-09-21 北京智芯原动科技有限公司 Method and device for identifying traffic signs
CN105975941A (en) * 2016-05-31 2016-09-28 电子科技大学 Multidirectional vehicle model detection recognition system based on deep learning
CN106022285A (en) * 2016-05-30 2016-10-12 北京智芯原动科技有限公司 Vehicle type identification method and vehicle type identification device based on convolutional neural network
CN106056751A (en) * 2016-05-20 2016-10-26 聚龙股份有限公司 Prefix number identification method and system
CN106156744A (en) * 2016-07-11 2016-11-23 西安电子科技大学 SAR target detection method based on CFAR detection with degree of depth study
CN106372666A (en) * 2016-08-31 2017-02-01 同观科技(深圳)有限公司 Target identification method and device
CN106372402A (en) * 2016-08-30 2017-02-01 中国石油大学(华东) Parallelization method of convolutional neural networks in fuzzy region under big-data environment
CN106446514A (en) * 2016-08-31 2017-02-22 中国石油大学(华东) Fuzzy theory and neural network-based well-log facies recognition method
CN106443598A (en) * 2016-12-08 2017-02-22 中国人民解放军海军航空工程学院 Convolutional neural network based cooperative radar network track deception jamming discrimination method
CN106503710A (en) * 2016-10-26 2017-03-15 北京邮电大学 A kind of automobile logo identification method and device
CN106557768A (en) * 2016-11-25 2017-04-05 北京小米移动软件有限公司 The method and device is identified by word in picture
CN106651774A (en) * 2016-12-27 2017-05-10 深圳市捷顺科技实业股份有限公司 License plate super-resolution model reconstruction method and device
CN106934392A (en) * 2017-02-28 2017-07-07 西交利物浦大学 Vehicle-logo recognition and attribute forecast method based on multi-task learning convolutional neural networks
CN106991420A (en) * 2017-03-27 2017-07-28 新智认知数据服务有限公司 A kind of detection method of license plate of the license plate area regression technique based on piecemeal
CN107134144A (en) * 2017-04-27 2017-09-05 武汉理工大学 A kind of vehicle checking method for traffic monitoring
CN107153873A (en) * 2017-05-08 2017-09-12 中国科学院计算技术研究所 A kind of two-value convolutional neural networks processor and its application method
CN107590456A (en) * 2017-09-06 2018-01-16 张栖瀚 Small micro- mesh object detection method in a kind of high-altitude video monitoring
CN107590492A (en) * 2017-08-28 2018-01-16 浙江工业大学 A kind of vehicle-logo location and recognition methods based on convolutional neural networks
CN107871125A (en) * 2017-11-14 2018-04-03 深圳码隆科技有限公司 Architecture against regulations recognition methods, device and electronic equipment
CN108156130A (en) * 2017-03-27 2018-06-12 上海观安信息技术股份有限公司 Network attack detecting method and device
CN108403111A (en) * 2018-02-01 2018-08-17 华中科技大学 A kind of epileptic electroencephalogram (eeg) recognition methods and system based on convolutional neural networks
US10157441B2 (en) 2016-12-27 2018-12-18 Automotive Research & Testing Center Hierarchical system for detecting object with parallel architecture and hierarchical method thereof
CN109389014A (en) * 2017-08-10 2019-02-26 杭州海康威视数字技术股份有限公司 Apply detection method, device and the electronic equipment of license plate vehicle
CN105809704B (en) * 2016-03-30 2019-03-15 北京小米移动软件有限公司 Identify the method and device of image definition
CN110084215A (en) * 2019-05-05 2019-08-02 上海海事大学 A kind of pedestrian of the twin network model of binaryzation triple recognition methods and system again
CN110261749A (en) * 2019-07-24 2019-09-20 广东电网有限责任公司 A kind of GIS partial discharge fault identification model building method, device and fault recognition method
CN111091150A (en) * 2019-12-12 2020-05-01 哈尔滨市科佳通用机电股份有限公司 Railway wagon cross rod cover plate fracture detection method

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090208058A1 (en) * 2004-04-15 2009-08-20 Donnelly Corporation Imaging system for vehicle
CN104134067A (en) * 2014-07-07 2014-11-05 河海大学常州校区 Road vehicle monitoring system based on intelligent visual Internet of Things

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090208058A1 (en) * 2004-04-15 2009-08-20 Donnelly Corporation Imaging system for vehicle
CN104134067A (en) * 2014-07-07 2014-11-05 河海大学常州校区 Road vehicle monitoring system based on intelligent visual Internet of Things

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
D.F.LLORCA等: "Vehicle logo recognition in traffic images using HOG features and SVM", 《IEEE》 *
YANN LECUN等: "Gradient-based learning applied to document recognition", 《IEEE》 *
卢雅琴等: "基于数学形态学的车牌定位方法", 《计算机工程》 *
彭博: "基于深度学习的车标识别方法研究", 《计算机科学》 *

Cited By (40)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105678344A (en) * 2016-02-29 2016-06-15 浙江群力电气有限公司 Intelligent classification method for power instrument equipment
WO2017166586A1 (en) * 2016-03-30 2017-10-05 乐视控股(北京)有限公司 Image identification method and system based on convolutional neural network, and electronic device
CN105868785A (en) * 2016-03-30 2016-08-17 乐视控股(北京)有限公司 Image identification method based on convolutional neural network and image identification system thereof
CN105809704B (en) * 2016-03-30 2019-03-15 北京小米移动软件有限公司 Identify the method and device of image definition
CN105956524A (en) * 2016-04-22 2016-09-21 北京智芯原动科技有限公司 Method and device for identifying traffic signs
CN105957238A (en) * 2016-05-20 2016-09-21 聚龙股份有限公司 Banknote management method and system
CN106056751B (en) * 2016-05-20 2019-04-12 聚龙股份有限公司 The recognition methods and system of serial number
CN106056751A (en) * 2016-05-20 2016-10-26 聚龙股份有限公司 Prefix number identification method and system
CN105957238B (en) * 2016-05-20 2019-02-19 聚龙股份有限公司 A kind of paper currency management method and its system
CN106022285A (en) * 2016-05-30 2016-10-12 北京智芯原动科技有限公司 Vehicle type identification method and vehicle type identification device based on convolutional neural network
CN105975941A (en) * 2016-05-31 2016-09-28 电子科技大学 Multidirectional vehicle model detection recognition system based on deep learning
CN106156744A (en) * 2016-07-11 2016-11-23 西安电子科技大学 SAR target detection method based on CFAR detection with degree of depth study
CN106156744B (en) * 2016-07-11 2019-01-29 西安电子科技大学 SAR target detection method based on CFAR detection and deep learning
CN106372402B (en) * 2016-08-30 2019-04-30 中国石油大学(华东) The parallel method of fuzzy region convolutional neural networks under a kind of big data environment
CN106372402A (en) * 2016-08-30 2017-02-01 中国石油大学(华东) Parallelization method of convolutional neural networks in fuzzy region under big-data environment
CN106446514A (en) * 2016-08-31 2017-02-22 中国石油大学(华东) Fuzzy theory and neural network-based well-log facies recognition method
CN106372666A (en) * 2016-08-31 2017-02-01 同观科技(深圳)有限公司 Target identification method and device
CN106372666B (en) * 2016-08-31 2019-07-19 同观科技(深圳)有限公司 A kind of target identification method and device
CN106503710A (en) * 2016-10-26 2017-03-15 北京邮电大学 A kind of automobile logo identification method and device
CN106557768A (en) * 2016-11-25 2017-04-05 北京小米移动软件有限公司 The method and device is identified by word in picture
CN106443598A (en) * 2016-12-08 2017-02-22 中国人民解放军海军航空工程学院 Convolutional neural network based cooperative radar network track deception jamming discrimination method
US10157441B2 (en) 2016-12-27 2018-12-18 Automotive Research & Testing Center Hierarchical system for detecting object with parallel architecture and hierarchical method thereof
CN106651774A (en) * 2016-12-27 2017-05-10 深圳市捷顺科技实业股份有限公司 License plate super-resolution model reconstruction method and device
CN106934392B (en) * 2017-02-28 2020-05-26 西交利物浦大学 Vehicle logo identification and attribute prediction method based on multi-task learning convolutional neural network
CN106934392A (en) * 2017-02-28 2017-07-07 西交利物浦大学 Vehicle-logo recognition and attribute forecast method based on multi-task learning convolutional neural networks
CN106991420A (en) * 2017-03-27 2017-07-28 新智认知数据服务有限公司 A kind of detection method of license plate of the license plate area regression technique based on piecemeal
CN108156130A (en) * 2017-03-27 2018-06-12 上海观安信息技术股份有限公司 Network attack detecting method and device
CN107134144A (en) * 2017-04-27 2017-09-05 武汉理工大学 A kind of vehicle checking method for traffic monitoring
CN107153873A (en) * 2017-05-08 2017-09-12 中国科学院计算技术研究所 A kind of two-value convolutional neural networks processor and its application method
CN107153873B (en) * 2017-05-08 2018-06-01 中国科学院计算技术研究所 A kind of two-value convolutional neural networks processor and its application method
CN109389014A (en) * 2017-08-10 2019-02-26 杭州海康威视数字技术股份有限公司 Apply detection method, device and the electronic equipment of license plate vehicle
CN107590492A (en) * 2017-08-28 2018-01-16 浙江工业大学 A kind of vehicle-logo location and recognition methods based on convolutional neural networks
CN107590492B (en) * 2017-08-28 2019-11-19 浙江工业大学 A kind of vehicle-logo location and recognition methods based on convolutional neural networks
CN107590456A (en) * 2017-09-06 2018-01-16 张栖瀚 Small micro- mesh object detection method in a kind of high-altitude video monitoring
CN107590456B (en) * 2017-09-06 2020-09-18 张栖瀚 Method for detecting small and micro targets in high-altitude video monitoring
CN107871125A (en) * 2017-11-14 2018-04-03 深圳码隆科技有限公司 Architecture against regulations recognition methods, device and electronic equipment
CN108403111A (en) * 2018-02-01 2018-08-17 华中科技大学 A kind of epileptic electroencephalogram (eeg) recognition methods and system based on convolutional neural networks
CN110084215A (en) * 2019-05-05 2019-08-02 上海海事大学 A kind of pedestrian of the twin network model of binaryzation triple recognition methods and system again
CN110261749A (en) * 2019-07-24 2019-09-20 广东电网有限责任公司 A kind of GIS partial discharge fault identification model building method, device and fault recognition method
CN111091150A (en) * 2019-12-12 2020-05-01 哈尔滨市科佳通用机电股份有限公司 Railway wagon cross rod cover plate fracture detection method

Similar Documents

Publication Publication Date Title
Payet et al. From contours to 3d object detection and pose estimation
CN104766058B (en) A kind of method and apparatus for obtaining lane line
CN106407931B (en) A kind of depth convolutional neural networks moving vehicle detection method
Timofte et al. Multi-view traffic sign detection, recognition, and 3D localisation
CN103366602B (en) Method of determining parking lot occupancy from digital camera images
Chen et al. Vehicle detection in high-resolution aerial images via sparse representation and superpixels
CN102609686B (en) Pedestrian detection method
CN106650721B (en) A kind of industrial character identifying method based on convolutional neural networks
CN105956560B (en) A kind of model recognizing method based on the multiple dimensioned depth convolution feature of pondization
CN105139028B (en) SAR image sorting technique based on layering sparseness filtering convolutional neural networks
CN104299008B (en) Vehicle type classification method based on multi-feature fusion
CN102663377B (en) Character recognition method based on template matching
CN104599275B (en) The RGB-D scene understanding methods of imparametrization based on probability graph model
CN108564097B (en) Multi-scale target detection method based on deep convolutional neural network
CN104866810A (en) Face recognition method of deep convolutional neural network
CN103034863B (en) The remote sensing image road acquisition methods of a kind of syncaryon Fisher and multiple dimensioned extraction
CN106935035B (en) Parking offense vehicle real-time detection method based on SSD neural network
CN106250812A (en) A kind of model recognizing method based on quick R CNN deep neural network
CN105809198B (en) SAR image target recognition method based on depth confidence network
CN100414561C (en) Vehicle plate extracting method based on skiagraphy and mathematical morphology
CN105373794A (en) Vehicle license plate recognition method
CN106096602A (en) A kind of Chinese licence plate recognition method based on convolutional neural networks
CN106022232A (en) License plate detection method based on deep learning
CN108509978A (en) The multi-class targets detection method and model of multi-stage characteristics fusion based on CNN
CN104778721A (en) Distance measuring method of significant target in binocular image

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20160224