CN105022992A - Automatic identification method of vehicle license plate - Google Patents
Automatic identification method of vehicle license plate Download PDFInfo
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- CN105022992A CN105022992A CN201510371609.4A CN201510371609A CN105022992A CN 105022992 A CN105022992 A CN 105022992A CN 201510371609 A CN201510371609 A CN 201510371609A CN 105022992 A CN105022992 A CN 105022992A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/24—Aligning, centring, orientation detection or correction of the image
- G06V10/242—Aligning, centring, orientation detection or correction of the image by image rotation, e.g. by 90 degrees
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/14—Image acquisition
- G06V30/148—Segmentation of character regions
- G06V30/153—Segmentation of character regions using recognition of characters or words
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- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
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- Image Analysis (AREA)
Abstract
The invention provides an automatic identification method of a vehicle license plate. A plurality of steps of shooting, analog-to-digital conversion, preprocessing, positioning, inclined correction, character segmentation and recognition and the like are performed on a vehicle, and then complete automatic recognition of the vehicle license plate is achieved; the positioning method is achieved through a gray limit, a position limit, close operation and area filtering; and the preprocessing method comprises gray processing and gray stretching, and the gray stretching is achieved through adoption of a gray adjustment function imadjust () in the matlab. The automatic identification method has the advantages of the good recognition effect and the high degree of automation, and does not need artificial participation. The automatic identification method can be applied to a traffic monitoring field and also can be applied to other detection and recognition fields; and the automatic identification method has a wide application prospect.
Description
Technical field
The present invention relates to image processing field, particularly relate to a kind of car plate automatic identification method.
Background technology
Along with the dramatic change of traffic environment and instrument, the quantity of automobile grows with each passing day, and adopts Vehicle License Plate Recognition System to the inexorable trend that automobile carries out intellectuality, automatic management becomes social development.
License plate recognition technology (Vehicle License Plate Recognition, VLPR) is the one application of Video Image recognition technology in License Plate Identification.
License plate recognition technology requires the license plate in motion to be extracted and identifies from complex background, by technology such as license plate retrieving, Image semantic classification, feature extraction, Recognition of License Plate Characters, identify vehicle identification number, this technology is widely applied in vehicle on highway management, multiple field such as electronic charging (ETC) system, parking lot management etc.
As the Main Means identifying testing vehicle register, license plate recognition technology advances traffic administration to one of gordian technique of intelligent development.Pass through Car license recognition, many important informations of vehicle can be obtained, thus greatly can promote the intelligence degree of transportation system management, current, the development that Car license recognition is existing larger technically, and in the face of day by day complicated traffic environment, the complexity of image, the power of light, the real current situation of car plate and the realization of the travel speed of vehicle to Recognition Algorithm of License Plate are had higher requirement.
License plate recognition technology requires the license plate in motion to be extracted and identifies from complex background, by pre-service, License Plate, character cutting, identification, thus finally identify vehicle license, and the performance that outstanding license plate recognition technology all possesses excellence in recognition correct rate, recognition time, recognition speed etc. can be applied among the recognition system with reality.
Summary of the invention
Technical matters to be solved by this invention is to invent a kind of car plate automatic identification method, thus robotization obtains the important information of vehicle, promotes the intelligence degree in traffic administration.
The present invention is achieved in that a kind of car plate automatic identification method, comprises the steps:
S1. by ball machine monitoring vehicular traffic, when detecting that vehicle passes through the target area of presetting, judge vehicle heading, and according to the gunlock that vehicle heading startup respective direction configures, described vehicle is taken pictures; Described vehicle license plate background color is blue, and license board information is white;
S2. the image photographed is carried out quantification and becomes digital form image by described gunlock, and transfers to graphics processing unit;
S3. pre-service is carried out to described image;
S4. pretreated image is positioned, obtain doubtful license plate area;
S5. slant correction is carried out to the image of doubtful license plate area;
S6. Character segmentation is carried out to the car plate in doubtful license plate area;
S7. character is identified;
Described preprocess method is:
S31. gray processing is carried out to described image, described gray processing method is for getting coloured image R, G, B three-component, and be each a point of flux matched weighting coefficient, the three-component weighted value of trying to achieve is as the component after the gray processing of pixel, and each pixel only has the difference in brightness; Described weighting coefficient is by user's sets itself as required;
S32. carry out gray scale stretching to the image after gray processing, gray scale Tuning function imadjust () that described gray scale stretching is carried by use matlab realizes;
Described localization method is:
S41. threshold process is carried out to the image after gray scale stretching, filter ineligible gray-scale value, only retain the pixel that threshold value meets the demands;
S42. in the pixel met the demands, only retain the pixel being positioned at picture bottom 1/4 scope;
S43. carry out morphology closed operation to image, the structuring element shape of described morphology closed operation is rectangle;
S44. carry out area filter to image, obtain doubtful license plate area, the length breadth ratio of described doubtful license plate area falls into default aspect ratio range.
Preferably, the three-component weighting coefficient of described R, G, B is 0.229,0.587,0.114.
Preferably, described default aspect ratio range is 2.2 ~ 3.7.
Preferably, described threshold value comprises upper threshold value and lower threshold value.
Preferably, described lower threshold value is 80 ~ 120, and described upper threshold value is for being 220-255.
Preferably, described sloped correcting method is:
Extract the edge of described image, adopt license plate image described in radon transfer pair to carry out slant correction process, add up described image radon and convert the maximal value obtained, record angle of inclination now, thus to described correct image.
Preferably, S6 comprises use closure operation, and corrosion erasing is not the part of automotive license plate; In S6, the Chinese character on car plate, letter and number are revised, cut out the concrete border of each character in car plate, according to each character boundary, slant correction is carried out to each character.
Preferably, in S7, the character in the template base of each character and pre-stored is compared, thus complete Car license recognition.
Preferably, described threshold value is provided with default value, but can reset according to actual needs.
Implement the present invention, there is following beneficial effect:
The invention provides a kind of car plate automatic identification method, by carrying out automatic camera, automatically location to car plate and automatically identifying, realize the full-automatic identification of car plate.Recognition effect of the present invention is good, and do not need artificial participation, automaticity is high.The present invention both can be applied to traffic monitoring field also can be applied to other detection and Identification fields, has broad application prospects.
Embodiment
For making the object, technical solutions and advantages of the present invention clearly, below the present invention is described in further detail.
Embodiments provide a kind of car plate automatic identification method, comprise the steps:
S1. by ball machine monitoring vehicular traffic, when detecting that vehicle passes through the target area of presetting, judge vehicle heading, and according to the gunlock that vehicle heading startup respective direction configures, described vehicle is taken pictures; Described vehicle license plate background color is blue, and license board information is white;
S2. the image photographed is carried out quantification and becomes digital form image by described gunlock, and transfers to graphics processing unit;
S3. pre-service is carried out to described image;
S4. pretreated image is positioned, obtain doubtful license plate area;
S5. slant correction is carried out to the image of doubtful license plate area;
S6. Character segmentation is carried out to the car plate in doubtful license plate area;
S7. character is identified;
Described preprocess method is:
S31. gray processing is carried out to described image, described gray processing method is for getting coloured image R, G, B three-component, and be each a point of flux matched weighting coefficient, the three-component weighted value of trying to achieve is as the component after the gray processing of pixel, and each pixel only has the difference in brightness; Described weighting coefficient is by user's sets itself as required;
S32. carry out gray scale stretching to the image after gray processing, gray scale Tuning function imadjust () that described gray scale stretching is carried by use matlab realizes;
Described localization method is:
S41. threshold process is carried out to the image after gray scale stretching, filter ineligible gray-scale value, only retain the pixel that threshold value meets the demands;
S42. in the pixel met the demands, only retain the pixel being positioned at picture bottom 1/4 scope;
S43. carry out morphology closed operation to image, the structuring element shape of described morphology closed operation is rectangle;
S44. carry out area filter to image, obtain doubtful license plate area, the length breadth ratio of described doubtful license plate area falls into default aspect ratio range.
Preferably, the three-component weighting coefficient of described R, G, B is 0.229,0.587,0.114.
Preferably, described default aspect ratio range is 2.2 ~ 3.7.
Preferably, described threshold value comprises upper threshold value and lower threshold value.
Preferably, described lower threshold value is 80 ~ 120, and described upper threshold value is for being 220-255.
Preferably, described sloped correcting method is:
Extract the edge of described image, adopt license plate image described in radon transfer pair to carry out slant correction process, add up described image radon and convert the maximal value obtained, record angle of inclination now, thus to described correct image.
Preferably, S6 comprises use closure operation, and corrosion erasing is not the part of automotive license plate; In S6, the Chinese character on car plate, letter and number are revised, cut out the concrete border of each character in car plate, according to each character boundary, slant correction is carried out to each character.
Preferably, in S7, the character in the template base of each character and pre-stored is compared, thus complete Car license recognition.
Preferably, described threshold value is provided with default value, but can reset according to actual needs.
Above disclosedly be only present pre-ferred embodiments, certainly can not limit the interest field of the present invention with this, therefore according to the equivalent variations that the claims in the present invention are done, still belong to the scope that the present invention is contained.
Claims (9)
1. a car plate automatic identification method, is characterized in that, comprises the steps:
S1. by ball machine monitoring vehicular traffic, when detecting that vehicle passes through the target area of presetting, judge vehicle heading, and according to the gunlock that vehicle heading startup respective direction configures, described vehicle is taken pictures; Described vehicle license plate background color is blue, and license board information is white;
S2. the image photographed is carried out quantification and becomes digital form image by described gunlock, and transfers to graphics processing unit;
S3. pre-service is carried out to described image;
S4. pretreated image is positioned, obtain doubtful license plate area;
S5. slant correction is carried out to the image of doubtful license plate area;
S6. Character segmentation is carried out to the car plate of doubtful license plate area;
S7. character is identified;
Described preprocess method is:
S31. gray processing is carried out to described image, described gray processing method is for getting coloured image R, G, B three-component, and be each a point of flux matched weighting coefficient, the three-component weighted value of trying to achieve is as the component after the gray processing of pixel, and each pixel only has the difference in brightness; Described weighting coefficient is by user's sets itself as required;
S32. carry out gray scale stretching to the image after gray processing, gray scale Tuning function imadjust () that described gray scale stretching is carried by use matlab realizes;
Described localization method is:
S41. threshold process is carried out to the image after gray scale stretching, filter ineligible gray-scale value, only retain the pixel that threshold value meets the demands;
S42. in the pixel met the demands, only retain the pixel being positioned at picture bottom 1/4 scope;
S43. carry out morphology closed operation to image, the structuring element shape that described morphology closed operation uses is rectangle;
S44. carry out area filter to image, obtain doubtful license plate area, the length breadth ratio of described doubtful license plate area falls into default aspect ratio range.
2. a kind of car plate automatic identification method according to claim 1, is characterized in that, the three-component weighting coefficient of described R, G, B is 0.229,0.587,0.114.
3. a kind of car plate automatic identification method according to claim 1, is characterized in that, described default aspect ratio range is 2.2 ~ 3.7.
4. a kind of car plate automatic identification method according to claim 1, it is characterized in that, described threshold value comprises upper threshold value and lower threshold value.
5. a kind of car plate automatic identification method according to claim 4, is characterized in that, described lower threshold value is 80 ~ 120, and described upper threshold value is for being 220-255.
6. a kind of car plate automatic identification method according to claim 5, it is characterized in that, described sloped correcting method is:
Extract the edge of described image, adopt license plate image described in radon transfer pair to carry out slant correction process, add up described image radon and convert the maximal value obtained, record angle of inclination now, thus to described correct image.
7. a kind of car plate automatic identification method according to claim 6, it is characterized in that, S6 comprises use closure operation, and corrosion erasing is not the part of automotive license plate; In S6, the Chinese character on car plate, letter and number are revised, cut out the concrete border of each character in car plate, according to each character boundary, slant correction is carried out to each character.
8. a kind of car plate automatic identification method according to claim 7, is characterized in that, is compared by the character in the template base of each character and pre-stored, thus complete Car license recognition in S7.
9. a kind of car plate automatic identification method according to claim 8, it is characterized in that, described threshold value is provided with default value, but can reset according to actual needs.
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Cited By (4)
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CN105684058A (en) * | 2016-01-19 | 2016-06-15 | 冯旋宇 | License plate identifying method and license plate identifying system for intelligent traffic |
CN106846607A (en) * | 2017-01-11 | 2017-06-13 | 深圳怡化电脑股份有限公司 | A kind of Paper Currency Identification and device |
CN111652230A (en) * | 2020-05-25 | 2020-09-11 | 浙江大华技术股份有限公司 | License plate recognition method, electronic device and storage medium |
CN117351438A (en) * | 2023-10-24 | 2024-01-05 | 武汉无线飞翔科技有限公司 | Real-time vehicle position tracking method and system based on image recognition |
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Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105684058A (en) * | 2016-01-19 | 2016-06-15 | 冯旋宇 | License plate identifying method and license plate identifying system for intelligent traffic |
CN106846607A (en) * | 2017-01-11 | 2017-06-13 | 深圳怡化电脑股份有限公司 | A kind of Paper Currency Identification and device |
CN111652230A (en) * | 2020-05-25 | 2020-09-11 | 浙江大华技术股份有限公司 | License plate recognition method, electronic device and storage medium |
CN111652230B (en) * | 2020-05-25 | 2023-05-12 | 浙江大华技术股份有限公司 | License plate recognition method, electronic device and storage medium |
CN117351438A (en) * | 2023-10-24 | 2024-01-05 | 武汉无线飞翔科技有限公司 | Real-time vehicle position tracking method and system based on image recognition |
CN117351438B (en) * | 2023-10-24 | 2024-06-04 | 武汉无线飞翔科技有限公司 | Real-time vehicle position tracking method and system based on image recognition |
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Application publication date: 20151104 |