CN109508715A - A kind of License Plate and recognition methods based on deep learning - Google Patents

A kind of License Plate and recognition methods based on deep learning Download PDF

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CN109508715A
CN109508715A CN201811281994.3A CN201811281994A CN109508715A CN 109508715 A CN109508715 A CN 109508715A CN 201811281994 A CN201811281994 A CN 201811281994A CN 109508715 A CN109508715 A CN 109508715A
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license plate
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闵卫东
李祥鹏
赵浩宇
黄杰
韩清
刘瑞康
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Nanchang University
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Abstract

The present invention provides a kind of License Plate based on deep learning and recognition methods, including two aspects: License Plate based on Faster R-CNN model and based on the end-to-end Recognition of License Plate Characters of AlexNet-L network model；Step 1, to the license plate area size application k-means++ algorithm process of training set, optimal candidate's size is selected, Faster R-CNN is integrated to；Step 2, using Faster R-CNN training picture, model is obtained；Step 3, it inputs the picture comprising license plate and obtains characteristic pattern after process of convolution；Step 4, the characteristic pattern that will acquire is by RPN network output license plate position and score；Step 5, license plate area is intercepted from original image；Step 6, the license plate area of interception passes through the end-to-end convolutional neural networks of AlexNet-L network model, each character on final output license plate, the present invention carries out License Plate using the method based on Faster R-CNN, using the end-to-end license plate character recognition method of AlexNet-L, the accuracy rate to License Plate and character recognition can be effectively improved.

Description

A kind of License Plate and recognition methods based on deep learning
Technical field
The present invention relates to field of license plate recognition, specially a kind of License Plate based on deep learning and identification side Method.
Background technique
Vehicle License Plate Recognition System (Vehicle License Plate Recognition, VLPR) refer to be able to detect that by It monitors the vehicle on road surface and automatically extracts vehicle license information (containing chinese character, English alphabet, Arabic numerals and number plate face Color) technology that is handled.Car license recognition is one of the important component in modern intelligent transportation system, and application is very wide It is general.It is based on the technologies such as Digital Image Processing, pattern-recognition, computer vision, to the vehicle image of shot by camera Or video sequence is analyzed, and each unique license plate number of automobile is obtained, to complete identification process.After some Parking lot fee collection management, the measurement of magnitude of traffic flow Con trolling index, vehicle location, automobile burglar, high speed may be implemented in continuous processing means Highway hypervelocity automation supervision, electronic eye used for catching red light runner, toll station etc. function.Maintenance traffic safety and city are controlled Peace prevents traffic jam, realizes that traffic automation management has the meaning of reality.
Registration number is unique " identity " mark of vehicle, and licence plate automatic identification technology can not made any in automobile The automatic registration and verifying of automobile " identity " are realized in the case where change, this technology has been applied to highway toll, parking pipe The various occasions such as reason, weighing system, traffic guidance, traffic administration, highway inspection, vehicle scheduling, vehicle detection.
The existing licence plate recognition method based on License Plate Character Segmentation, overwhelming majority experiment is carried out under ideal environment 's.Situations such as once it is fuzzy to encounter such as license plate, being illuminated by the light influences greatly, license plate sloped, the accuracy rate of Car license recognition will be significantly Ground reduces.In addition to this, characters on license plate can not correctly divide the effect that will also directly affect Recognition of License Plate Characters.
Summary of the invention
The purpose of the present invention is to provide a kind of License Plate based on deep learning and recognition methods, to solve above-mentioned back The problem of proposing in scape technology, the present invention selects best license plate area size using k-means++ algorithm, in conjunction with Faster R-CNN improves AlexNet network model to carry out License Plate, designs end and end based on AlexNet-L License plate character recognition method.
To achieve the above object, the invention provides the following technical scheme: a kind of License Plate and knowledge based on deep learning Other method, including two aspects: the License Plate based on Faster R-CNN model and the end based on AlexNet-L network model The Recognition of License Plate Characters of opposite end；
Step 1, to the license plate area size application k-means++ algorithm process of training set, optimal candidate's size is selected, It is integrated to Faster R-CNN；
Step 2, using Faster R-CNN training picture, model is obtained；
Step 3, it inputs the picture comprising license plate and obtains characteristic pattern after process of convolution；
Step 4, resulting characteristic pattern is obtained into candidate frame, the spy that will acquire after full convolutional neural networks RPN processing Sign figure and candidate frame are classified after ROI pooling processing together, export license plate position and score；
Step 5, license plate area is intercepted from original image；
Step 6, the license plate area of interception passes through the end-to-end convolutional neural networks of AlexNet-L network model, finally Export each character on license plate.
It further, is approximately 3: 1, in order to definitely reflect since the license plate area ratio of China is 440cm*140cm The size of license plate area in various pictures, the length-width ratio of 3 ratios is selected using k-means++ algorithm；
K-means++ algorithm needs to use the overlapping rate (IoU) of the candidate frame of model prediction and the candidate frame of label to refer to Mark, the calculation of IoU are as follows:
Wherein, SgroundTruthIndicate true candidate frame, SanchorBoxIndicate the candidate frame of prediction；
K-means++ algorithm obtain the algorithm of initial candidate frame the following steps are included:
The input of S1:k-means++ algorithm is the length and width of license plate
C={ d1(x1, y1), d2(x2, y2) ..., dn(xn, yn) and k candidate frame length-width ratio；
S2: it is c that a sample is randomly selected from C1(c1∈C)；
S3: for each sample in C, each sample is calculated to c1Distance:
d(bi, c1)=1-Io U (bi, c1) (2)
Wherein i ∈ (1,2,3 ..., n)；
S4: the probability that each sample is selected as next mass center is calculated:
S5: S is definedi:
S6: the random number r between one 0 to 1 is generated, judges that r belongs to region { si-1, si, then bi(xi, yi) it is second Mass center:
S7: step S3~S6 is repeated, until obtaining k mass center.
Further, the RPN network in the Faster R-CNN model, is substantially in convolutional neural networks CNN On the basis of increase Quan Juan base cls and reg layers, wherein cls layers be for judging that candidate frame is prospect or background, and Reg layers are for finely tuning candidate frame；
The loss function of Faster R-CNN are as follows:
Wherein, i indicates an index of anchor, piIt is the prediction probability of i-th of anchor, if anchor is positive, Value be 1, conversely,Value be 0, tiIndicate 4 parameter coordinates of predicted boundary frame,It indicates corresponding with positive anchor The coordinate vector of groud-truth box, Classification Loss LclsIt is that the logarithm of 2 classifications (target and non-targeted) loses.
Further, the AlexNet-L network model is an improvement in AlexNet network models, AlexNet-L network model has nine layers of structure, and first and second layer contains convolution, pond layer and normalization；With AlexNet net Unlike network model, the sequence of pond layer and normalization operation is different in the first layer of AlexNet-L network model； The third layer of AlexNet-L network model is to three identical convolution operations of layer 5；Layer 6 used convolutional layer and Pond layer, layer 7 are a full articulamentums, and the 8th and the 9th layer, using seven full articulamentums arranged side by side, is respectively intended to license plate Each character identified.
Further, the AlexNet-L network model carries out following aspects in AlexNet network models Improvement:
1. the improvement of first layer and the second layer:
By normalization and the mutual reversed order of pond layer in first layer in AlexNet and the second layer, i.e., will return in AlexNet One change 1, pond layer 1 and normalization 2,2 reversed order of pond layer；
2. increasing convolutional layer to improve classifying quality:
Increase a same convolutional layer behind the convolutional layer 3 of AlexNet, convolutional layer 4；
3. the improvement of full articulamentum:
The end-to-end character recognition of license plate is 7 characters, and the full articulamentum of the layer 7 of AlexNet is changed to 7 simultaneously The full articulamentum of column obtains the feature vector of 7 characters respectively；
4. the improvement of output layer:
Since the characters on license plate of China has 7 characters, final output should be 7 labels, by the of AlexNet network Eight full articulamentums are changed to 7 full articulamentums arranged side by side, and are respectively connected with 7 full articulamentums arranged side by side of preceding layer, for The last one full articulamentum, the corresponding number of tags of each classification be not unique.
Further, the output layer of the AlexNet-L network model is classified using Softmax regression function, Soffmax Formula are as follows:
G represents classification number, and d is the training function of g；
The calculating process of convolution operation in AlexNet-L network model are as follows:
O=(I+2 × P-K)/S+1 (7)
Wherein, O is the size (length or width of picture) of output data, and I is the size (length of picture of input data Or width), P pad represents whether in width and height both sides filler pixels, K kernel_size, indicates convolution sum Size, S stride, indicate step-length；
The operation calculating process of pond layer are as follows:
O=(I-K)/S+1 (8)
Wherein, the meaning of each parameter is in formula (7)；
In AlexNet-L network, the activation primitive being connected with each convolutional layer is selected with ReLu:
ReLu (x)=Max (x, 0) (9)
Compared with prior art, the beneficial effects of the present invention are:
The present invention carries out License Plate using the method based on Faster R-CNN, is chosen using k-means++ algorithm Best license plate area carries out License Plate, the accuracy rate of Lai Tigao License Plate, using being based in conjunction with Faster R-CNN On the one hand the end-to-end license plate character recognition method of the improved AlexNet-L of AlexNet can be reduced existing based on license plate License Plate Character Segmentation result in the licence plate recognition method of Character segmentation influences Recognition of License Plate Characters；It on the other hand, can be with Improve the accuracy rate of Car license recognition.
Detailed description of the invention
Fig. 1 is that the present invention is based on the License Plates of deep learning and identification framework figure；
Fig. 2 is Faster R-CNN illustraton of model of the present invention；
Fig. 3 is that k-means++ algorithm combination license plate of the present invention obtains anchors procedure chart；
Fig. 4 is 9 anchors dimensional drawings of the invention；
Fig. 5 is AlexNet network architecture figure of the present invention；
Fig. 6 is that the present invention is based on the Recognition of License Plate Characters frame diagrams of AlexNet-L.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right The present invention is described in further detail.The specific embodiments are only for explaining the present invention technical solution described herein, and It is not limited to the present invention.
The present invention provides a kind of technical solution: a kind of License Plate and recognition methods based on deep learning, including two Aspect: the License Plate based on Faster R-CNN model and the end-to-end characters on license plate based on AlexNet-L network model Identification, total algorithm frame are as shown in Figure 1；
Step 1, to the license plate area size application k-means++ algorithm process of training set, optimal candidate's size is selected, It is integrated to Faster R-CNN；
Step 2, using Faster R-CNN training picture, model is obtained；
Step 3, it inputs the picture comprising license plate and obtains characteristic pattern after process of convolution；
Step 4, resulting characteristic pattern is obtained into candidate frame, the spy that will acquire after full convolutional neural networks RPN processing Sign figure and candidate frame are classified after ROI pooling processing together, export license plate position and score；
Step 5, license plate area is intercepted from original image；
Step 6, the license plate area of interception passes through the end-to-end convolutional neural networks of AlexNet-L network model, finally Export each character on license plate.
Existing Faster R-CNN model is not related to the related application of license plate, gets the bid first from data set thus Determine license plate area, then FasterR-CNN is applied among the license plate area data set got, finally obtains every license plate The candidate region of picture.
Further, the present invention analyzes the size of license plate area, convergence rate and License Plate when in order to improve trained Accuracy rate, license plate area size is integrated in Faster R-CNN, due to China license plate area ratio be 440cm* 140cm is approximately 3: 1, in order to definitely reflect the size of license plate area in various pictures, is selected using k-means++ algorithm The length-width ratio of fixed 3 ratios；
K-means++ algorithm needs to use the overlapping rate (IoU) of the candidate frame of model prediction and the candidate frame of label to refer to Mark, the calculation of IoU are as follows:
Wherein, SgroundTruthIndicate true candidate frame, SanchorBoxIndicate the candidate frame of prediction；
The process of the processing of k-means++ algorithm combination license plate is as shown in Figure 3.
K-means++ algorithm obtain the algorithm of initial candidate frame the following steps are included:
The input of S1:k-means++ algorithm is the length and width of license plate
C={ d1(x1, y1), d2(x2, y2) ..., dn(xn, yn) and k candidate frame length-width ratio；
S2: it is c that a sample is randomly selected from C1(c1∈C)；
S3: for each sample in C, each sample is calculated to c1Distance:
d(bi, c1)=1-Io U (bi, c1) (2)
Wherein i ∈ (1,2,3 ..., n)；
S4: the probability that each sample is selected as next mass center is calculated:
S5: S is definedi:
S6: the random number r between one 0 to 1 is generated, judges that r belongs to region { si-1, si, then bi(xi, yi) it is second Mass center:
S7: step S3~S6 is repeated, until obtaining k mass center.
By experiment, selecting k is 3, using the size of k-means++ algorithm picks license plate area, obtain three it is different Length-width ratio (2.2,3.2,4.6), the length-width ratio after rounding are (2,3,5), and the anchor size that benchmark is arranged is 8*8, use Scale is [16,24,40], 9 anchors of each sliding window generation, as shown in Figure 4.
The advantage of Faster R-CNN is not only in that candidate frame to extract partially to be put on GPU and runs, also region candidate The extraction part of frame is embedded into inside network outside network, and the characteristic pattern after convolution can be used to obtain region candidate frame.
Further, the RPN network in the FasterR-CNN model, is substantially in convolutional neural networks CNN On the basis of increase Quan Juan base cls and reg layers, wherein cls layers are and the reg for judging that candidate frame is prospect or background Layer is for finely tuning candidate frame；Faster R-CNN model is as shown in Figure 2.
The loss function of Faster R-CNN are as follows:
Wherein, i indicates an index of anchor, piIt is the prediction probability of i-th of anchor, if anchor is positive, Value be 1, conversely,Value be 0, tiIndicate 4 parameter coordinates of predicted boundary frame,It indicates corresponding with positive anchor The coordinate vector of groud-truth box, Classification Loss LclsIt is that the logarithm of 2 classifications (target and non-targeted) loses.
AlexNet is the convolutional neural networks model based on classification that Krizhevsky et al. is proposed, and obtains 2012 The champion of year ImageNet match.AlexNet structure is as shown in Figure 5.
AlexNet has eight layers of structure, and the 1st, 2 layer has used convolutional layer (conv), pond layer (pool) and normalization behaviour Make (norm)；3rd, 4 layer the same, contains convolutional layer；5th layer uses convolutional layer and pond layer；6-8 layers apply and connect entirely Meet layer (fc).
Although AlexNet network significant effect on target classification, it is simultaneously not applied among Recognition of License Plate Characters, There is no the modellings for having specific meanings；The present invention improves and structure again on the basis of AlexNet network model It builds, proposes a kind of convolutional neural networks model AlexNet-L dedicated for Recognition of License Plate Characters of enhancing.
Further, the AlexNet-L network model is an improvement in AlexNet network models, is had Conducive to the accuracy for the identification for improving characters on license plate；AlexNet-L network model has nine layers of structure, and first and second layer contains Convolution, pond layer and normalization；Unlike AlexNet network model, pond in the first layer of AlexNet-L network model The sequence of layer and normalization operation is different；The third layer of AlexNet-L network model is to three identical volumes of layer 5 Product operation；Layer 6 has used convolutional layer and pond layer, and layer 7 is a full articulamentum, and the 8th and the 9th layer uses side by side Seven full articulamentums are respectively intended to identify that overall framework is as shown in Figure 6 to each character of license plate.
Further, the AlexNet-L network model carries out following aspects in AlexNet network models Improvement:
1. the improvement of first layer and the second layer:
By normalization and the mutual reversed order of pond layer in first layer in AlexNet and the second layer, identification essence can be improved Degree, while but also normalization operation, which reduces, calculates time and memory, therefore 1 will be normalized in AlexNet, pond layer 1 with And normalization 2,2 reversed order of pond layer；
2. increasing convolutional layer to improve classifying quality:
In order to improve the classifying quality of last Recognition of License Plate Characters, increase by one on the convolutional layer 3 of AlexNet, convolutional layer 4 The identical convolutional layer of layer；Due to convolutional layer 3, convolutional layer 4 is better than other layers in terms of classification, after convolutional layer 3, convolutional layer 4 Face increases a same convolutional layer, to find the expression of more vehicle license plate characteristics；
3. the improvement of full articulamentum:
The end-to-end character recognition of license plate is 7 characters, and the full articulamentum of the layer 7 of AlexNet is changed to 7 simultaneously The full articulamentum of column obtains the feature vector of 7 characters respectively；Because of the Chinese character in characters on license plate, letter and number feature is complete Complete different, processing, which is equivalent to, so separately handles the feature of each character, is conducive to improve the accurate of Recognition of License Plate Characters Rate；
4. the improvement of output layer:
Since the characters on license plate of China has 7 characters, final output should be 7 labels, by the of AlexNet network Eight full articulamentums are changed to 7 full articulamentums arranged side by side, and are respectively connected with 7 full articulamentums arranged side by side of preceding layer, for The last one full articulamentum, the corresponding number of tags of each classification be not unique.
In order to verify each validity improved to Recognition of License Plate Characters in AlexNet-L network model, for Corresponding control variate method analysis has been done in the improvement each time of AlexNet-L, does following groups experiment respectively:
First group of experiment: by AlexNet improve in 4. as one group of experiment；
Second group of experiment: by AlexNet improve in be 1. 4. combined together as one group of experiment；
The experiment of third group: by AlexNet improve in the be 2. 4. combined together as one group of experiment；
4th group of experiment: by AlexNet improve in be 3. 4. combined together as one group of experiment；
1. 2. 3. 4. 5th group of experiment: will be combined together as one group of experiment, the experiment of this group is also AlexNet-L net Network.
Experimental result is as shown in table 1, and Chinese character represents the 1st character of license plate, and letter represents the 2nd character of license plate, word Mother+number represents the remaining character of license plate, accuracy rate=correct number/test number.
Table 1 is directed to several groups of comparative experimentss of AlexNet network improvement
From table 1 it follows that the improvement in second group to the 4th group experiment for AlexNet network, to characters on license plate The result of identification is better than first group of experimental result, illustrates the improvement to AlexNet network model to Recognition of License Plate Characters Accuracy rate increases；The accuracy rate of 5th group of experimental result is significantly better than other four groups of experiments, illustrates AlexNet-L network mould Type is conducive to improve the accuracy rate of Recognition of License Plate Characters to the improvement of AlexNet.
Car license recognition of the present invention is based primarily upon Chinese license plate (inland and Taiwan), is all made of 7 last two layers of AlexNet-L Full articulamentum arranged side by side respectively corresponds the 1-7 character of inland license plate, rather than by the 1-7 character of license plate using same The recognition accuracy of single classification character can be improved in one full articulamentum in this way.
The present invention has also done comparative experiments on common data sets Road Patrol (RP).Because what RP data set was collected The license plate of TaiWan, China, TaiWan, China license plate shares 6 characters, and wherein preceding 4 characters of license plate are number, most latter two Character is letter, so being changed to 6 full articulamentums arranged side by side for last two layers of AlexNet-L network.
Further, the output layer of the AlexNet-L network model is classified using Softmax regression function, Soffmax Function can solve polytypic problem, and classical Sigmoid regression function can only solve the problems, such as two classification, Soffmax Formula are as follows:
G represents classification number, and d is the training function of g；
The calculating process of convolution operation in AlexNet-L network model are as follows:
O=(I+2 × P-K)/S+1 (7)
Wherein, O is the size (length or width of picture) of output data, and I is the size (length of picture of input data Or width), P pad represents whether in width and height both sides filler pixels, K kemel_size, indicates convolution sum Size, S stride, indicate step-length；
The operation calculating process of pond layer are as follows:
O=(I-K)/S+1 (8)
Wherein, the meaning of each parameter is in formula (7)；
In AlexNet-L network, the activation primitive that is connected with each convolutional layer selection ReLu, due to Sigmoid and Tanh activation primitive there are speed in gradient descent procedures slow, the problem more than the number of iterations, using ReLu function as activating Speed and efficiency can be improved in function, and the period is greatly shortened, ReLu function are as follows:
ReLu (x)=Max (x, 0) (9)
Input data size for AlexNet-L network is 227 × 227 × 3, by level 1 volume product core number: 96, Size: 11, step-length: 4, the size of the characteristic pattern after convolution is 55 × 55 × 96, and after ReLU activation operation, size of data is not Become, using convolution kernel size: 3, step-length: 2, the size of the characteristic pattern of acquisition is 27 × 27 × 96, special by normalized The size for levying figure is constant, so final input data size is 27 × 27 × 96.
The 2nd layer of AlexNet-L is similar with the 1st layer, and the input of 256 5 × 5 convolution kernels is 27 × 27 × 96 features Figure, further extracts feature, because level 2 volume product is all filled with 2 pixels in width and height both sides, the spy after convolution The size for levying figure is 27 × 27 × 256, and after ReLU activation operation, size of data is constant, collects core size using rolling up: 3, step It is long: 2, the size of the characteristic pattern of acquisition is 13 × 13 × 256, and by normalized, the size of characteristic pattern is constant, so finally Input data size be 13 × 13 × 256.
3-5 layers the same, activates and operates by convolution sum ReLU, and layer 6 is by convolution kernel and ReLU activation operation Afterwards, it joined pond layer, by 3-6, the size of data of output is 6 × 6 × 256, using 7-8 layers of arranged side by side 7 Full articulamentum, the convolution kernel number of the last each label of output layer from front to back are followed successively by 31,26,36,36,36,36,36.
In present invention experiment, the data set of test is from the data set and common data sets RP collected, from the data collected Collection is all Chinese inland license plate.
In order to verify the validity of Faster R-CNN combination VGG network, the present invention is directed to Faster R-CNN combination VGG Do the comparative experiments of License Plate respectively with ZF network.In Faster R-CNN training process, the number of iterations is set as 200000.In changing preceding 160000, Study rate parameter is set as in 0.001,40000 iteration, learning rate 0.0001, Momentum parameter is set as 0.9, and weight attenuation parameter is set as 0.0005.It is that 2440 pictures are tested in test set, It is resulting that the results are shown in Table 2.Wherein, recall rate meaning is that algorithm is determined as license plate and determines that correct license plate number accounts for survey The ratio of lump picture number is tried, accuracy rate meaning is that algorithm is determined as license plate and determines that correct license plate number accounts for algorithm judgement For the ratio of license plate number, the time spent in time is characters on license plate in one picture of average test.
License Plate comparative experiments of the table 2 based on Faster R-CNN
Algorithm Recall rate Accuracy rate Time (/s)
VGG 0.9909 0.9887 0.0321
ZF 0.9762 0.9778 0.0143
Although from Table 2, it can be seen that Faster R-CNN combination VGG network test the time spent in ratio Faster per second The time spent in R-CNN combination ZF network, is long, but accuracy rate of the Faster R-CNN combination VGG network in terms of License Plate Much higher than Faster R-CNN combination ZF network, so the present invention uses the license plate based on Faster R-CNN combination VGG network Localization method.
Table 3 is algorithms of different from the comparative experiments of license plate locating accuracy on the data set and RP data set collected.
License plate locating accuracy comparison (%) on 3 different data collection of table
From table 3 it is observed that the License Plate accuracy rate of inventive algorithm on different data sets is above other calculations Method.
To sum up, fixed in various natural scenes and the enterprising driving board of different data collection, inventive algorithm is in License Plate effect Be significantly better than that other several algorithms, overall performance are superior in accuracy rate.
In the experiment of AlexNet-L network model, as follows, maximum number of iterations 200000 is arranged in network parameter, initially Learning rate is 0.001.Then when iteration 20000 times, learning rate is changed to 0.1 times of last learning rate, Momentum parameter is set as 0.9, and weight attenuation parameter is set as 0.0005.In addition to this, the present invention also provides a comparison of license plate word Symbol segmentation combines the experiment of ANN neural metwork training characters on license plate, and License Plate Character Segmentation adds the Recognition of License Plate Characters of template matching Algorithm, License Plate Character Segmentation add the Recognition of License Plate Characters algorithm of SVM.These types of algorithm is respectively in the test set and RP number from collection It is tested according to collection, experimental result is as shown in table 4 and table 5.In table 4, Chinese character represents the 1st character of license plate, and letter represents 2nd character of license plate, letter+number represent the remaining character of license plate.Accuracy rate=correct number/test number, total character Refer to the result that all characters that license plate includes are correctly validated.Time indicates the mean time of average single license plate test Between.In table 5, total character represents 6 characters in RP data set and identifies correct result.
Table 4 compares (%) from the accuracy rate collected on data set
Algorithm Chinese character Letter Letter+number Total character Time (s)
The present invention 0.952 9 0.973 8 0.969 2 0.950 8 0.019 2
Artificial neural network 0.891 8 0.927 0 0.932 0 0.876 2 0.037 8
Character mother plate matching 0.914 3 0.944 7 0.936 9 0.909 4 0.029 6
SVM 0.950 0 0.966 4 0.955 3 0.946 0 0.064 3
Accuracy rate on 5 RP data set of table compares (%)
Algorithm Total character Time (s)
The present invention 0.9774 0.0136
Artificial neural network 0.9516 0.0289
Character mother plate matching 0.9583 0.0187
SVM 0.9651 0.0421
As can be seen that the single character of the license plate totality recognition accuracy of inventive algorithm and license plate from table 4 and table 5 Recognition accuracy is above other comparison algorithms.In addition to this, when test needed for the average Recognition of License Plate Characters of inventive algorithm Between less than other comparison algorithm.Show that the Recognition of License Plate Characters algorithm of end-to-end AlexNet-L proposed by the invention exists There is superiority in its relevant application.
The above only expresses the preferred embodiment of the present invention, and the description thereof is more specific and detailed, but can not be because This and be interpreted as limitations on the scope of the patent of the present invention.It should be pointed out that for those of ordinary skill in the art, Under the premise of not departing from present inventive concept, several deformations can also be made, improves and substitutes, these belong to protection of the invention Range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.

Claims (6)

1. a kind of License Plate and recognition methods based on deep learning, it is characterised in that: including two aspects: being based on Faster The License Plate of R-CNN model and end-to-end Recognition of License Plate Characters based on AlexNet-L network model；
Step 1, to the license plate area size application k-means++ algorithm process of training set, optimal candidate's size is selected, in conjunction with To Faster R-CNN；
Step 2, using Faster R-CNN training picture, model is obtained；
Step 3, it inputs the picture comprising license plate and obtains characteristic pattern after process of convolution；
Step 4, resulting characteristic pattern is obtained into candidate frame, the characteristic pattern that will acquire after full convolutional neural networks RPN processing Classify after ROI pooling processing together with candidate frame, exports license plate position and score；
Step 5, license plate area is intercepted from original image；
Step 6, the license plate area of interception passes through the end-to-end convolutional neural networks of AlexNet-L network model, final output Each character on license plate.
2. a kind of License Plate and recognition methods based on deep learning according to claim 1, it is characterised in that: due to The license plate area ratio of China is 440cm*140cm, is approximately 3: 1, in order to definitely reflect license plate area in various pictures Size selectes the length-width ratio of 3 ratios using k-means++ algorithm；
K-means++ algorithm needs to use the overlapping rate (IoU) of the candidate frame of model prediction and the candidate frame of label for index, The calculation of IoU are as follows:
Wherein, SgroundTruthIndicate true candidate frame, SanchorBoxIndicate the candidate frame of prediction；
K-means++ algorithm obtain the algorithm of initial candidate frame the following steps are included:
The input of S1:k-means++ algorithm is the length and width C={ d of license plate1(x1, y1), d2(x2, y2) ..., dn(xn, yn) and k candidate frame length-width ratio；
S2: it is c that a sample is randomly selected from C1(c1∈C)；
S3: for each sample in C, each sample is calculated to c1Distance:
d(bi, c1)=1-IoU (bi, c1) (2)
Wherein i ∈ (1,2,3 ..., n)；
S4: the probability that each sample is selected as next mass center is calculated:
S5: S is definedi:
S6: the random number r between one 0 to 1 is generated, judges that r belongs to region { si-1, si, then bi(xi, yi) it is second mass center；
S7: step S3~S6 is repeated, until obtaining k mass center.
3. a kind of License Plate and recognition methods based on deep learning according to claim 1, it is characterised in that: described RPN network in Faster R-CNN model is substantially to increase Quan Juan base on the basis of convolutional neural networks CNN Cls and reg layers, wherein cls layers are for judging that candidate frame is prospect or background, and reg layers are for finely tuning candidate frame；
The loss function of Faster R-CNN are as follows:
Wherein, i indicates an index of anchor, piIt is the prediction probability of i-th of anchor, if anchor is positive,Value be 1, conversely,Value be 0, tiIndicate 4 parameter coordinates of predicted boundary frame,Indicate groud- corresponding with positive anchor The coordinate vector of truth box, Classification Loss LclsIt is that the logarithm of 2 classifications (target and non-targeted) loses.
4. a kind of License Plate and recognition methods based on deep learning according to claim 1, it is characterised in that: described AlexNet-L network model is an improvement in AlexNet network models, and AlexNet-L network model has nine layers Structure, first and second layer contains convolution, pond layer and normalization；Unlike AlexNet network model, AlexNet-L The sequence of pond layer and normalization operation is different in the first layer of network model；The third layer of AlexNet-L network model arrives Three identical convolution operations of layer 5；Layer 6 has used convolutional layer and pond layer, and layer 7 is a full articulamentum, 8th and the 9th layer, using seven full articulamentums arranged side by side, is respectively intended to identify each character of license plate.
5. a kind of License Plate and recognition methods based on deep learning according to claim 4, it is characterised in that: described AlexNet-L network model carries out improvement following aspects in AlexNet network models:
1. the improvement of first layer and the second layer:
By normalization and the mutual reversed order of pond layer in first layer in AlexNet and the second layer, i.e., will be normalized in AlexNet 1, pond layer 1 and normalization 2,2 reversed order of pond layer；
2. increasing convolutional layer to improve classifying quality:
Increase a same convolutional layer behind the convolutional layer 3 of AlexNet, convolutional layer 4；
3. the improvement of full articulamentum:
The end-to-end character recognition of license plate is 7 characters, by the full articulamentum of the layer 7 of AlexNet be changed to 7 it is arranged side by side Full articulamentum obtains the feature vector of 7 characters respectively；
4. the improvement of output layer:
Since the characters on license plate of China has 7 characters, final output should be 7 labels, by the 8th of AlexNet network Full articulamentum is changed to 7 full articulamentums arranged side by side, and is respectively connected with 7 full articulamentums arranged side by side of preceding layer, for last One full articulamentum, the corresponding number of tags of each classification be not unique.
6. a kind of License Plate and recognition methods based on deep learning according to claim 4 or 5, it is characterised in that: The output layer of the AlexNet-L network model is classified using Softmax regression function, Softmax formula are as follows:
G represents classification number, and d is the training function of g；
The calculating process of convolution operation in AlexNet-L network model are as follows:
O=(I+2 × P-K)/S+1 (7)
Wherein, O is the size (length or width of picture) of output data, and I is the size (length or width of picture of input data Degree), P pad is represented whether in width and height both sides filler pixels, and K kemel_size indicates the big of convolution sum Small, S stride indicates step-length；
The operation calculating process of pond layer are as follows:
O=(I-K)/S+1 (8)
Wherein, the meaning of each parameter is in formula (7)；
In AlexNet-L network, the activation primitive being connected with each convolutional layer is selected with ReLu:
ReLu (x)=Max (x, 0) (9).
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