Summary of the invention
Lower to the accuracy rate of road traffic sign detection in order to solve the problems, such as, the first object of the present invention is to provide a kind of baseIn the high-precision method for traffic sign detection of deep learning.This method greatly increases the accuracy rate and detection essence of feature extractionDegree.
A kind of high-precision method for traffic sign detection based on deep learning of the invention, comprising:
Step 1: it acquires the Traffic Sign Images of history and carries out overlapping cutting in proportion, then be input to SSD network, untilObtain optimal SSD network parameter；
Step 2: extracted using the optimal SSD network of parameter cutting after Traffic Sign Images traffic sign feature and byAccording to whether being that traffic sign feature is divided into these two types of traffic sign features of positive and negative classification and stores to Traffic Sign Images testing numberAccording to library；
Step 3: using Traffic Sign Images Test database come training convolutional neural networks, until obtaining optimal convolutionNeural network parameter；
Step 4: the traffic indication map after overlapping is cut in proportion in video flowing is extracted using the optimal SSD network of parameterThe traffic sign feature of picture, then the traffic sign feature of extraction is input to the optimal convolutional neural networks of parameter, and then extractThe feature of traffic sign feature out；
Step 5: by the feature for the traffic sign feature extracted and Traffic Sign Images Test database it is positive and negative this twoClass traffic sign feature is matched respectively, and retains positive class traffic sign feature；
Step 6: in the Traffic Sign Images before final testing result to be restored to overlapping cutting in proportion.
Further, in the step 1, the Traffic Sign Images of collected history are subjected to overlapping cutting in proportionLater, position coordinates of the Traffic Sign Images in the Traffic Sign Images before overlapping cutting after saving cutting.
The present invention increases target object and accounts for original image by using there is the cutting method of overlapping to cut imageRatio can effectively improve the accuracy rate of feature extraction, and save the Traffic Sign Images after cutting before overlapping cuttingPosition coordinates in Traffic Sign Images are that accurate reproduction Traffic Sign Images improve coordinate basis.
Further, it during the step 1 training SSD network, is trained first using default parameters, according toTraining intermediate result is constantly adjusted initial weight, training rate and the number of iterations, until the SSD network is with defaultEfficiency reach preset recognition effect.
SSD network parameter that in this way can be optimal, and then accurately extract in video flowing the friendship after overlapping cutting in proportionThe traffic sign feature of logical sign image is the accuracy rate of traffic sign feature extraction and the basis that detection accuracy is established.
Further, it during step 3 training convolutional neural networks, is instructed first using default parametersPractice, according to training intermediate result, initial weight, training rate and the number of iterations are constantly adjusted, until the convolution mindPreset recognition effect is reached with preset efficiency through network.
Convolutional neural networks parameter that in this way can be optimal, and then the traffic sign for accurately extracting Traffic Sign Images is specialThe feature of sign is the accuracy rate of traffic sign feature extraction and the basis that detection accuracy is established.
The second object of the present invention is to provide a kind of high-precision road traffic sign detection system based on deep learning.
A kind of high-precision road traffic sign detection system based on deep learning of the invention, comprising:
SSD network training module is used to acquire the Traffic Sign Images of history and carries out overlapping cutting in proportion, then defeatedEnter to SSD network, until obtaining optimal SSD network parameter；
Tagsort module is used to extract the friendship of Traffic Sign Images after cutting using the optimal SSD network of parameterLogical flag sign and according to whether being divided into positive and negative classification these two types traffic sign feature for traffic sign feature and store to trafficSign image Test database；
Convolutional neural networks training module is used for using Traffic Sign Images Test database come training convolutional nerve netNetwork, until obtaining optimal convolutional neural networks parameter；
Characteristic extracting module is used to be extracted using the optimal SSD network of parameter in video flowing and is overlapped cutting in proportionThe traffic sign feature of Traffic Sign Images afterwards, then the traffic sign feature of extraction is input to the optimal convolutional Neural of parameterNetwork, and then extract the feature of traffic sign feature；
Characteristic matching module, the feature and Traffic Sign Images detection data of the traffic sign feature for being used to extractPositive and negative these two types traffic sign feature is matched respectively in library, and retains positive class traffic sign feature；
Image restoring module, the traffic indication map before being used to for final testing result being restored to overlapping cutting in proportionAs in.
Further, which further includes cutting traffic indication map image position logging modle, is used to go through collectedAfter the Traffic Sign Images of history carry out overlapping cutting in proportion, the Traffic Sign Images after saving cutting are before overlapping cuttingPosition coordinates in Traffic Sign Images.
The present invention increases target object and accounts for original image by using there is the cutting method of overlapping to cut imageRatio can effectively improve the accuracy rate of feature extraction, and save the Traffic Sign Images after cutting before overlapping cuttingPosition coordinates in Traffic Sign Images are that accurate reproduction Traffic Sign Images improve coordinate basis.
Further, it in the SSD network training module, is trained first using default parameters, according in trainingBetween as a result, to initial weight, training rate and the number of iterations be constantly adjusted, until the SSD network is with preset efficiencyReach preset recognition effect.
SSD network parameter that in this way can be optimal, and then accurately extract in video flowing the friendship after overlapping cutting in proportionThe traffic sign feature of logical sign image is the accuracy rate of traffic sign feature extraction and the basis that detection accuracy is established.
Further, it in the convolutional neural networks training module, is trained first using default parameters, according to instructionPractice intermediate result, initial weight, training rate and the number of iterations is constantly adjusted, until the convolutional neural networks are with pre-If efficiency reach preset recognition effect.
Convolutional neural networks parameter that in this way can be optimal, and then the traffic sign for accurately extracting Traffic Sign Images is specialThe feature of sign is the accuracy rate of traffic sign feature extraction and the basis that detection accuracy is established.
The second object of the present invention is to provide a kind of high-precision road traffic sign detection system based on deep learning.
High-precision road traffic sign detection system the present invention also provides another kind based on deep learning.
Another high-precision road traffic sign detection system based on deep learning of the invention, comprising:
Image collecting device, is configured as the Traffic Sign Images of acquisition history, and is sent to server；
The server, is configured as:
Overlapping cutting is carried out by the Traffic Sign Images of the history of acquisition and in proportion, then is input to SSD network, untilTo optimal SSD network parameter；
Extracted using the optimal SSD network of parameter cutting after Traffic Sign Images traffic sign feature and according to whetherIt is divided into these two types of traffic sign features of positive and negative classification for traffic sign feature and stores to Traffic Sign Images Test database；
Using Traffic Sign Images Test database come training convolutional neural networks, until obtaining optimal convolutional Neural netNetwork parameter；
The friendship of the Traffic Sign Images after overlapping is cut in proportion in video flowing is extracted using the optimal SSD network of parameterLogical flag sign, then the traffic sign feature of extraction is input to the optimal convolutional neural networks of parameter, and then extract trafficThe feature of flag sign；
By these two types of traffic positive and negative in the feature for the traffic sign feature extracted and Traffic Sign Images Test databaseFlag sign is matched respectively, and retains positive class traffic sign feature；
In Traffic Sign Images before final testing result to be restored to overlapping cutting in proportion.
Further, the server is also configured in proportion carry out the Traffic Sign Images of collected historyPosition coordinates of the Traffic Sign Images in the Traffic Sign Images before overlapping cutting after overlapping cutting, after saving cutting.
The present invention increases target object and accounts for original image by using there is the cutting method of overlapping to cut imageRatio can effectively improve the accuracy rate of feature extraction, and save the Traffic Sign Images after cutting before overlapping cuttingPosition coordinates in Traffic Sign Images are that accurate reproduction Traffic Sign Images improve coordinate basis.
Further, the server is also configured to use default parameters first during training SSD networkIt is trained, according to training intermediate result, initial weight, training rate and the number of iterations is constantly adjusted, until describedSSD network reaches preset recognition effect with preset efficiency.
SSD network parameter that in this way can be optimal, and then accurately extract in video flowing the friendship after overlapping cutting in proportionThe traffic sign feature of logical sign image is the accuracy rate of traffic sign feature extraction and the basis that detection accuracy is established.
Further, the server, during being also configured to training convolutional neural networks, first using defaultParameter is trained, and according to training intermediate result, is constantly adjusted to initial weight, training rate and the number of iterations, untilThe convolutional neural networks reach preset recognition effect with preset efficiency.
Convolutional neural networks parameter that in this way can be optimal, and then the traffic sign for accurately extracting Traffic Sign Images is specialThe feature of sign is the accuracy rate of traffic sign feature extraction and the basis that detection accuracy is established.
SSD network of the present invention, full name in English are as follows: Single Shot MultiBox Detector network,It is after carrying out convolution to image using single convolutional neural networks, prediction is a series of not at each position of characteristic imageWith the bounding box of size and length-width ratio.In test phase, SSD network is to the object for separately including each classification in each bounding boxA possibility that body, is predicted, and is adjusted to bounding box to adapt to the shape of target object.
Compared with prior art, the beneficial effects of the present invention are:
The present invention increases target object and accounts for original image by using there is the cutting method of overlapping to cut imageRatio can effectively improve the accuracy rate of feature extraction, by SSD network and convolutional neural networks to Traffic Sign Images dataFeature extraction is carried out, and the testing result by obtaining after SSD training refreshes high-precision Traffic Sign Images database, greatlyImprove the accuracy rate and detection accuracy of feature extraction.
Specific embodiment
It is noted that following detailed description is all illustrative, it is intended to provide further instruction to the application.Unless anotherIt indicates, all technical and scientific terms used herein has usual with the application person of an ordinary skill in the technical fieldThe identical meanings of understanding.
It should be noted that term used herein above is merely to describe specific embodiment, and be not intended to restricted rootAccording to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singularAlso it is intended to include plural form, additionally, it should be understood that, when in the present specification using term "comprising" and/or " packetInclude " when, indicate existing characteristics, step, operation, device, component and/or their combination.
As background technique is introduced, the problem lower to the accuracy rate of road traffic sign detection exists in the prior art,In order to solve technical problem as above, the invention proposes a kind of high-precision method for traffic sign detection based on deep learning.
Fig. 1 is a kind of process signal of the high-precision method for traffic sign detection based on deep learning in the embodiment of the present inventionFigure, high-precision method for traffic sign detection of one of the present embodiment as shown in the figure based on deep learning may include:
S101 acquires the Traffic Sign Images of history and carries out overlapping cutting in proportion, then is input to SSD network, untilObtain optimal SSD network parameter.
In the specific implementation, acquiring the Traffic Sign Images of history and carrying out overlapping cutting in proportion, after cutting being overlappedTraffic Sign Images are stored to historical traffic sign image database.Then it is selected out of historical traffic sign image database againTake the sample of trained SSD network, the sample set of composing training SSD network.
It during training SSD network, is trained first using default parameters, according to training intermediate result, to firstBeginning weight, training rate and the number of iterations are constantly adjusted, until the SSD network reaches preset knowledge with preset efficiencyOther effect.SSD network parameter that in this way can be optimal, and then accurately extract in video flowing the traffic after overlapping cutting in proportionThe traffic sign feature of sign image is the accuracy rate of traffic sign feature extraction and the basis that detection accuracy is established.
S102, extracted using the optimal SSD network of parameter cutting after Traffic Sign Images traffic sign feature and byAccording to whether being that traffic sign feature is divided into these two types of traffic sign features of positive and negative classification and stores to Traffic Sign Images testing numberAccording to library.
In the specific implementation, the traffic sign for extracting Traffic Sign Images after cutting using the optimal SSD network of parameter is specialIt levies and stores to Traffic Sign Images Test database；For the traffic sign feature extracted using the optimal SSD network of parameter,If traffic sign feature, then the class that is positive traffic sign feature；If not traffic sign feature, then the class that is negative traffic sign is specialSign.
Wherein, these two types of traffic sign features of positive and negative classification are stored to Traffic Sign Images Test database.
S103, using Traffic Sign Images Test database come training convolutional neural networks, until obtaining optimal convolutionNeural network parameter.
In the specific implementation, the training set of training convolutional neural networks is chosen from Traffic Sign Images Test database, andIt is input in convolutional neural networks.Wherein, convolutional neural networks from be input to output include convolutional layer, pond layer, articulamentum andSoftmax returns classifier layer.Softmax returns classifier layer and there was only one layer, and the number of plies of convolutional layer, pond layer and articulamentumIt is set according to actual conditions or adjusts.Such as: convolutional neural networks include three-layer coil lamination, three layers of pond layer, three layers of full connectionLayer and classifier layer is returned positioned at last softmax.
During hands-on convolutional neural networks, it is trained first using default parameters, it is intermediate according to trainingAs a result, being constantly adjusted to initial weight, training rate and the number of iterations, until the convolutional neural networks are with preset effectRate reaches preset recognition effect.
Convolutional neural networks parameter that in this way can be optimal, and then the traffic sign for accurately extracting Traffic Sign Images is specialThe feature of sign is the accuracy rate of traffic sign feature extraction and the basis that detection accuracy is established.
S104 extracts the traffic indication map after overlapping is cut in proportion in video flowing using the optimal SSD network of parameterThe traffic sign feature of picture, then the traffic sign feature of extraction is input to the optimal convolutional neural networks of parameter, and then extractThe feature of traffic sign feature out.
In the specific implementation, after carrying out overlapping cutting in proportion in video flowing using the extraction of trained SSD networkTraffic sign feature, the traffic sign feature extracted according to SSD network, using trained convolutional neural networks to extraction bandFeature carry out further training screening.
S105, by positive and negative these two types in the feature for the traffic sign feature extracted and Traffic Sign Images Test databaseTraffic sign feature is matched respectively, and retains positive class traffic sign feature.
In the specific implementation, if the feature for the traffic sign feature extracted and positive class traffic sign characteristic matching, are extractedTo the feature of traffic sign feature retain the traffic sign feature, and then detect Traffic Sign Images.
Final testing result is restored in the Traffic Sign Images before overlapping is cut by S106 in proportion.
In another embodiment, it after the Traffic Sign Images of collected history being carried out overlapping cutting in proportion, protectsPosition coordinates of the Traffic Sign Images in the Traffic Sign Images before overlapping cutting after depositing cutting.
In this way by using there is the cutting method of overlapping to cut image, the ratio that target object accounts for original image is increasedExample can effectively improve the accuracy rate of feature extraction, and save friendship of the Traffic Sign Images after cutting before overlapping cuttingPosition coordinates in logical sign image are that accurate reproduction Traffic Sign Images improve coordinate basis.
The present embodiment combines depth learning technology with high-precision road traffic sign detection technology, by SSD network andConvolutional neural networks are trained, and are extracted after carrying out overlapping cutting in proportion in video flowing using trained SSD networkTraffic sign feature, the traffic sign feature extracted according to SSD network, utilize trained convolutional neural networks extract hand overThe feature of logical flag sign, by the feature for the traffic sign feature extracted and positive and negative the two of Traffic Sign Images Test databaseThe feature of class traffic sign is matched, and positive class traffic sign feature is retained, and obtains high-precision traffic sign matching the selection result,Effectively improve the accuracy rate of high-precision road traffic sign detection.
Fig. 2 is a kind of high-precision road traffic sign detection system structure signal based on deep learning in the embodiment of the present inventionFigure, high-precision road traffic sign detection system of one of the present embodiment as shown in the figure based on deep learning may include:
(1) SSD network training module is used to acquire the Traffic Sign Images of history and carries out overlapping cutting in proportion,It is input to SSD network again, until obtaining optimal SSD network parameter.
In the specific implementation, acquiring the Traffic Sign Images of history and carrying out overlapping cutting in proportion, after cutting being overlappedTraffic Sign Images are stored to historical traffic sign image database.Then it is selected out of historical traffic sign image database againTake the sample of trained SSD network, the sample set of composing training SSD network.
It during training SSD network, is trained first using default parameters, according to training intermediate result, to firstBeginning weight, training rate and the number of iterations are constantly adjusted, until the SSD network reaches preset knowledge with preset efficiencyOther effect.SSD network parameter that in this way can be optimal, and then accurately extract in video flowing the traffic after overlapping cutting in proportionThe traffic sign feature of sign image is the accuracy rate of traffic sign feature extraction and the basis that detection accuracy is established.
(2) tagsort module is used to extract Traffic Sign Images after cutting using the optimal SSD network of parameterTraffic sign feature and according to whether being divided into these two types of traffic sign features of positive and negative classification for traffic sign feature and store best friendLogical sign image Test database.
In the specific implementation, the traffic sign for extracting Traffic Sign Images after cutting using the optimal SSD network of parameter is specialIt levies and stores to Traffic Sign Images Test database；For the traffic sign feature extracted using the optimal SSD network of parameter,If traffic sign feature, then the class that is positive traffic sign feature；If not traffic sign feature, then the class that is negative traffic sign is specialSign.
Wherein, these two types of traffic sign features of positive and negative classification are stored to Traffic Sign Images Test database.
(3) convolutional neural networks training module is used for using Traffic Sign Images Test database come training convolutional mindThrough network, until obtaining optimal convolutional neural networks parameter.
In the specific implementation, the training set of training convolutional neural networks is chosen from Traffic Sign Images Test database, andIt is input in convolutional neural networks.Wherein, convolutional neural networks from be input to output include convolutional layer, pond layer, articulamentum andSoftmax returns classifier layer.Softmax returns classifier layer and there was only one layer, and the number of plies of convolutional layer, pond layer and articulamentumIt is set according to actual conditions or adjusts.Such as: convolutional neural networks include three-layer coil lamination, three layers of pond layer, three layers of full connectionLayer and classifier layer is returned positioned at last softmax.
During hands-on convolutional neural networks, it is trained first using default parameters, it is intermediate according to trainingAs a result, being constantly adjusted to initial weight, training rate and the number of iterations, until the convolutional neural networks are with preset effectRate reaches preset recognition effect.
Convolutional neural networks parameter that in this way can be optimal, and then the traffic sign for accurately extracting Traffic Sign Images is specialThe feature of sign is the accuracy rate of traffic sign feature extraction and the basis that detection accuracy is established.
(4) characteristic extracting module is used to be extracted using the optimal SSD network of parameter in video flowing to be overlapped in proportion and be cutThe traffic sign feature of Traffic Sign Images after cutting, then the traffic sign feature of extraction is input to the optimal convolution mind of parameterThrough network, and then extract the feature of traffic sign feature.
In the specific implementation, after carrying out overlapping cutting in proportion in video flowing using the extraction of trained SSD networkTraffic sign feature, the traffic sign feature extracted according to SSD network, using trained convolutional neural networks to extraction bandFeature carry out further training screening.
(5) characteristic matching module, the feature and Traffic Sign Images for the traffic sign feature for being used to extract detectPositive and negative these two types traffic sign feature is matched respectively in database, and retains positive class traffic sign feature.
In the specific implementation, if the feature for the traffic sign feature extracted and positive class traffic sign characteristic matching, are extractedTo the feature of traffic sign feature retain the traffic sign feature, and then detect Traffic Sign Images.
(6) image restoring module, the traffic mark before being used to for final testing result being restored to overlapping cutting in proportionIn will image.
In another embodiment, which further includes cutting traffic indication map image position logging modle, is used to acquireTo history Traffic Sign Images carry out overlapping cutting in proportion after, save cutting after Traffic Sign Images cut in overlappingThe position coordinates in Traffic Sign Images before cutting.
The present invention increases target object and accounts for original image by using there is the cutting method of overlapping to cut imageRatio can effectively improve the accuracy rate of feature extraction, and save the Traffic Sign Images after cutting before overlapping cuttingPosition coordinates in Traffic Sign Images are that accurate reproduction Traffic Sign Images improve coordinate basis.
The present embodiment combines depth learning technology with high-precision road traffic sign detection technology, by SSD network andConvolutional neural networks are trained, and are extracted after carrying out overlapping cutting in proportion in video flowing using trained SSD networkTraffic sign feature, the traffic sign feature extracted according to SSD network, utilize trained convolutional neural networks extract hand overThe feature of logical flag sign, by the feature for the traffic sign feature extracted and positive and negative the two of Traffic Sign Images Test databaseThe feature of class traffic sign is matched, and positive class traffic sign feature is retained, and obtains high-precision traffic sign matching the selection result,Effectively improve the accuracy rate of high-precision road traffic sign detection.
Fig. 3 is that another kind is illustrated based on the high-precision road traffic sign detection system structure of deep learning in the embodiment of the present inventionFigure, high-precision road traffic sign detection system of one of the present embodiment as shown in the figure based on deep learning may include:
(1) image collecting device, is configured as the Traffic Sign Images of acquisition history, and is sent to server.
Wherein, image collecting device can be realized using video camera, be used to acquire traffic mark image.
(2) server is configured as:
Overlapping cutting is carried out by the Traffic Sign Images of the history of acquisition and in proportion, then is input to SSD network, untilTo optimal SSD network parameter；
Extracted using the optimal SSD network of parameter cutting after Traffic Sign Images traffic sign feature and according to whetherIt is divided into these two types of traffic sign features of positive and negative classification for traffic sign feature and stores to Traffic Sign Images Test database；
Using Traffic Sign Images Test database come training convolutional neural networks, until obtaining optimal convolutional Neural netNetwork parameter；
The friendship of the Traffic Sign Images after overlapping is cut in proportion in video flowing is extracted using the optimal SSD network of parameterLogical flag sign, then the traffic sign feature of extraction is input to the optimal convolutional neural networks of parameter, and then extract trafficThe feature of flag sign；
By these two types of traffic positive and negative in the feature for the traffic sign feature extracted and Traffic Sign Images Test databaseFlag sign is matched respectively, and retains positive class traffic sign feature；
In Traffic Sign Images before final testing result to be restored to overlapping cutting in proportion.
In another embodiment, the server, be also configured to by the Traffic Sign Images of collected history by thanPosition of the Traffic Sign Images in the Traffic Sign Images before overlapping cutting after example carries out overlapping cutting, after saving cuttingCoordinate.
The present invention increases target object and accounts for original image by using there is the cutting method of overlapping to cut imageRatio can effectively improve the accuracy rate of feature extraction, and save the Traffic Sign Images after cutting before overlapping cuttingPosition coordinates in Traffic Sign Images are that accurate reproduction Traffic Sign Images improve coordinate basis.
In another embodiment, the server is also configured to during training SSD network, first using silentRecognize parameter to be trained, according to training intermediate result, initial weight, training rate and the number of iterations are constantly adjusted, directlyPreset recognition effect is reached with preset efficiency to the SSD network.
SSD network parameter that in this way can be optimal, and then accurately extract in video flowing the friendship after overlapping cutting in proportionThe traffic sign feature of logical sign image is the accuracy rate of traffic sign feature extraction and the basis that detection accuracy is established.
In another embodiment, the server during being also configured to training convolutional neural networks, makes firstIt is trained with default parameters, according to training intermediate result, initial weight, training rate and the number of iterations is constantly adjustedIt is whole, until the convolutional neural networks reach preset recognition effect with preset efficiency.
Convolutional neural networks parameter that in this way can be optimal, and then the traffic sign for accurately extracting Traffic Sign Images is specialThe feature of sign is the accuracy rate of traffic sign feature extraction and the basis that detection accuracy is established.
The present embodiment combines depth learning technology with high-precision road traffic sign detection technology, by SSD network andConvolutional neural networks are trained, and are extracted after carrying out overlapping cutting in proportion in video flowing using trained SSD networkTraffic sign feature, the traffic sign feature extracted according to SSD network, utilize trained convolutional neural networks extract hand overThe feature of logical flag sign, by the feature for the traffic sign feature extracted and positive and negative the two of Traffic Sign Images Test databaseThe feature of class traffic sign is matched, and positive class traffic sign feature is retained, and obtains high-precision traffic sign matching the selection result,Effectively improve the accuracy rate of high-precision road traffic sign detection.
It should be understood by those skilled in the art that, the embodiment of the present invention can provide as method, system or computer programProduct.Therefore, the shape of hardware embodiment, software implementation or embodiment combining software and hardware aspects can be used in the present inventionFormula.Moreover, the present invention, which can be used, can use storage in the computer that one or more wherein includes computer usable program codeThe form for the computer program product implemented on medium (including but not limited to magnetic disk storage and optical memory etc.).
The present invention be referring to according to the method for the embodiment of the present invention, the process of equipment (system) and computer program productFigure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructionsThe combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programsInstruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produceA raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for realThe device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spyDetermine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram orThe function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that countingSeries of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer orThe instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram oneThe step of function of being specified in a box or multiple boxes.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be withRelevant hardware is instructed to complete by computer program, the program can be stored in a computer-readable storage mediumIn, the program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein, the storage medium can be magneticDish, CD, read-only memory (Read-Only Memory, ROM) or random access memory (RandomAccessMemory, RAM) etc..
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects model to the present inventionThe limitation enclosed, those skilled in the art should understand that, based on the technical solutions of the present invention, those skilled in the art are notNeed to make the creative labor the various modifications or changes that can be made still within protection scope of the present invention.