CN114913518B - License plate recognition method, device, equipment and medium based on image processing - Google Patents
License plate recognition method, device, equipment and medium based on image processing Download PDFInfo
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
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- 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
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- G06V30/168—Smoothing or thinning of the pattern; Skeletonisation
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- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/18—Extraction of features or characteristics of the image
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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Abstract
The invention relates to the field of artificial intelligence, and provides a license plate recognition method, device, equipment and medium based on image processing, which can respond to received license plate images, perform brightness-based preprocessing on the license plate images to obtain first images, perform brightness compensation on the license plate images, avoid the accuracy of recognition due to the influence of brightness, position the first images to obtain license plate regions in the first images, correct the license plate regions to obtain second images so as to correct the license plate regions, avoid the interference of angles in the license plate recognition process, perform feature extraction on the second images to obtain target features, perform character recognition according to the target features to obtain license plate recognition results of the license plate images, and further realize license plate recognition based on artificial intelligence means, thereby improving recognition efficiency. In addition, the invention also relates to a blockchain technology, and a character template dictionary can be stored in the blockchain node.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a license plate recognition method, device, equipment and medium based on image processing.
Background
With the rapid development of highway traffic industry, intelligent traffic management systems have been the focus of attention, and vehicle license plate recognition systems have been widely used in real life as a part of intelligent traffic management systems.
However, due to the influence of photographing environment, the brightness and angle of license plate pictures of vehicles are different, and pictures with large brightness difference or skewed license plate angles can appear on the pictures.
In addition, the accuracy of license plate recognition is also affected to a certain extent because of poor picture quality.
Disclosure of Invention
In view of the above, it is necessary to provide a license plate recognition method, device, equipment and medium based on image processing, which aims to solve the problem of low license plate recognition accuracy.
The license plate recognition method based on the image processing comprises the following steps:
Responding to a received license plate image, and performing brightness-based preprocessing on the license plate image to obtain a first image;
Positioning the first image to obtain a license plate region in the first image;
correcting the license plate region to obtain a second image;
extracting features of the second image to obtain target features;
and carrying out character recognition according to the target features to obtain a license plate recognition result of the license plate image.
According to a preferred embodiment of the present invention, the preprocessing of the license plate image based on brightness includes:
Determining a cut-off of brightness in the license plate image;
dividing the license plate image into a first area and a second area based on the dividing line;
Acquiring a first threshold value corresponding to the brightness in the first area and a second threshold value corresponding to the brightness in the second area which are preset;
Calculating a difference between the second threshold and the first threshold;
When the difference value is larger than a third threshold value, carrying out brightness compensation on the license plate image; or alternatively;
And when the difference value is smaller than or equal to the third threshold value, not performing brightness compensation on the license plate image.
According to a preferred embodiment of the present invention, in the first region, the ratio of the number of pixels having a gray value greater than the first threshold to the total number of all pixels in the first region is a first value; in the second region, the ratio of the number of pixel points with gray values larger than the second threshold to the total number of pixel points in the second region is a second numerical value; wherein the first and second values are values between 0 and 1.
According to a preferred embodiment of the present invention, after performing brightness compensation on the license plate image, the performing brightness-based preprocessing on the license plate image further includes:
Sharpening the license plate image subjected to brightness compensation to obtain a first intermediate image;
and carrying out smooth filtering on the first intermediate image to obtain the first image.
According to a preferred embodiment of the present invention, the positioning the first image, and obtaining the license plate region in the first image includes:
detecting a blue region in the first image using an HSI color model;
Detecting a white region in the first image using an RGB model;
and obtaining a standard length-width ratio of the license plate, and adjusting the blue area and the white area according to the standard length-width ratio to obtain the license plate area.
According to a preferred embodiment of the present invention, the correcting the license plate region to obtain a second image includes:
identifying a blue background of the license plate region by using an HSV color model;
Performing horizontal expansion processing on the license plate region based on the blue background to obtain a second intermediate image;
performing horizontal differential operation on the second intermediate image to obtain edge information of the second intermediate image;
And rotating the second intermediate image based on the edge information to obtain the second image.
According to a preferred embodiment of the present invention, the performing character recognition according to the target feature, and obtaining the license plate recognition result of the license plate image includes:
Calling a pre-configured character template dictionary;
calculating the intersection of the target feature and each character template in the character template dictionary to obtain a first feature corresponding to each character template;
performing exclusive OR operation on the first characteristic corresponding to each character template and the target characteristic to obtain a second characteristic corresponding to each character template;
performing exclusive OR operation on the first features corresponding to each character template and each character template to obtain third features corresponding to each character template;
Determining the number of white pixels in each character template as a first number, the number of white pixels in the target feature as a second number, the number of white pixels in the first feature corresponding to each character template as a third number, the number of white pixels in the second feature corresponding to each character template as a fourth number, and the number of white pixels in the third feature corresponding to each character template as a fifth number;
Calculating the average of the first number, the second number and the third number corresponding to each character template to obtain an average value corresponding to each character template;
Calculating the square of the difference value of the first number corresponding to each character template and the average value corresponding to each character template to obtain the first square corresponding to each character template;
calculating the square of the difference value of the second number corresponding to each character template and the average value corresponding to each character template to obtain the second square corresponding to each character template;
Calculating the square of the difference value of the third number corresponding to each character template and the average value corresponding to each character template to obtain the third square corresponding to each character template;
calculating the quotient of the sum of the first square, the second square and the third square corresponding to each character template and a preset value, and calculating the arithmetic square root of the quotient to obtain the square root corresponding to each character template;
calculating the quotient of the fifth number corresponding to each character template and the first number corresponding to each character template to obtain a first quotient corresponding to each character template;
Calculating the quotient of the fourth number corresponding to each character template and the second number corresponding to each character template to obtain a second quotient corresponding to each character template;
Multiplying the square root corresponding to each character template, the first quotient and the second quotient to obtain a product corresponding to each character template;
Calculating the quotient of the product of the third number corresponding to each character template and the product corresponding to each character template to obtain the similarity between the target feature and each character template;
and generating a license plate recognition result of the license plate image according to the characters in the character template with the maximum similarity.
A license plate recognition device based on image processing, the license plate recognition device based on image processing comprising:
the preprocessing unit is used for responding to the received license plate image, preprocessing the license plate image based on brightness and obtaining a first image;
the positioning unit is used for positioning the first image to obtain a license plate region in the first image;
The correction unit is used for correcting the license plate region to obtain a second image;
The extraction unit is used for extracting the characteristics of the second image to obtain target characteristics;
And the recognition unit is used for carrying out character recognition according to the target characteristics to obtain a license plate recognition result of the license plate image.
A computer device, the computer device comprising:
a memory storing at least one instruction; and
And the processor executes the instructions stored in the memory to realize the license plate recognition method based on the image processing.
A computer-readable storage medium having stored therein at least one instruction that is executed by a processor in a computer device to implement the image processing-based license plate recognition method.
According to the technical scheme, the received license plate image can be responded, the license plate image is subjected to brightness-based preprocessing to obtain the first image, brightness compensation is performed on the license plate image, accuracy of recognition due to influence of brightness is avoided, the first image is positioned to obtain a license plate region in the first image, the license plate region is corrected to obtain the second image, the license plate region is corrected, angle interference in the license plate recognition process is avoided, feature extraction is performed on the second image to obtain target features, character recognition is performed according to the target features to obtain a license plate recognition result of the license plate image, further license plate recognition is achieved based on an artificial intelligent means, and recognition efficiency is improved.
Drawings
FIG. 1 is a flowchart of a license plate recognition method based on image processing according to a preferred embodiment of the present invention.
Fig. 2 is a functional block diagram of a license plate recognition device based on image processing according to a preferred embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a computer device for implementing a license plate recognition method based on image processing according to a preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a flowchart of a license plate recognition method based on image processing according to a preferred embodiment of the present invention. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs.
The license plate recognition method based on image processing is applied to one or more computer devices, wherein the computer device is a device capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and the hardware of the computer device comprises, but is not limited to, a microprocessor, an Application SPECIFIC INTEGRATED Circuit (ASIC), a Programmable gate array (Field-Programmable GATE ARRAY, FPGA), a digital Processor (DIGITAL SIGNAL Processor, DSP), an embedded device and the like.
The computer device may be any electronic product that can interact with a user in a human-computer manner, such as a Personal computer, a tablet computer, a smart phone, a Personal digital assistant (Personal DIGITAL ASSISTANT, PDA), a game console, an interactive internet protocol television (Internet Protocol Television, IPTV), a smart wearable device, etc.
The computer device may also include a network device and/or a user device. Wherein the network device includes, but is not limited to, a single network server, a server group composed of a plurality of network servers, or a Cloud based Cloud Computing (Cloud Computing) composed of a large number of hosts or network servers.
The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Wherein artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
The network in which the computer device is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (Virtual Private Network, VPN), and the like.
S10, responding to the received license plate image, and preprocessing the license plate image based on brightness to obtain a first image.
In this embodiment, the license plate image may be acquired by an image acquisition device (such as a monitoring camera) or may be uploaded by a related staff, which is not limited by the present invention.
In order to facilitate accurate identification of the license plate image, the license plate image needs to be preprocessed.
In at least one embodiment of the present invention, the preprocessing the license plate image based on brightness includes:
Determining a cut-off of brightness in the license plate image;
dividing the license plate image into a first area and a second area based on the dividing line;
Acquiring a first threshold value corresponding to the brightness in the first area and a second threshold value corresponding to the brightness in the second area which are preset;
Calculating a difference between the second threshold and the first threshold;
When the difference value is larger than a third threshold value, carrying out brightness compensation on the license plate image; or alternatively;
And when the difference value is smaller than or equal to the third threshold value, not performing brightness compensation on the license plate image.
When the difference is greater than the third threshold, it indicates that the luminance distribution of the license plate image is uneven, and there is a larger luminance difference (for example, the luminance of the upper half of the license plate image is darker, and an obvious boundary is formed between the upper half of the license plate image and the lower half of the license plate image), and at this time, if feature extraction is directly performed, features of some regions (for example, darker portions) cannot be extracted, which affects the accuracy of license plate image recognition.
When the difference value is smaller than or equal to the third threshold value, the brightness distribution of the license plate image is uniform, and brightness compensation is not needed.
The third threshold may be configured in a user-defined manner.
Through the embodiment, the brightness compensation can be performed on the license plate image, and the accuracy of identification due to the influence of brightness is avoided.
Specifically, in the first region, the ratio of the number of pixel points with gray values larger than the first threshold to the total number of all pixel points in the first region is a first numerical value; in the second region, the ratio of the number of pixel points with gray values larger than the second threshold to the total number of pixel points in the second region is a second numerical value; wherein the first and second values are values between 0 and 1.
The first value and the second value may also be configured according to actual requirements, which is not limited by the present invention.
Further, after performing brightness compensation on the license plate image, the performing brightness-based preprocessing on the license plate image further includes:
Sharpening the license plate image subjected to brightness compensation to obtain a first intermediate image;
and carrying out smooth filtering on the first intermediate image to obtain the first image.
Through the embodiment, after brightness compensation, the edge information of the license plate image can be enhanced through sharpening processing, and a complete boundary is formed, so that contour extraction of the license plate image is facilitated. Meanwhile, through smooth filtering, noise of the license plate image can be effectively eliminated.
S11, positioning the first image to obtain a license plate region in the first image.
After the preprocessing, the license plate region can be positioned based on the preprocessed first image.
In at least one embodiment of the present invention, the positioning the first image, and obtaining the license plate region in the first image includes:
detecting a blue region in the first image using an HSI (Hue-Saturation-INTENSITY (LIGHTNESS)) color model;
detecting a white region in the first image using a RGB (Red Green Blue) model;
and obtaining a standard length-width ratio of the license plate, and adjusting the blue area and the white area according to the standard length-width ratio to obtain the license plate area.
Wherein the standard aspect ratio may be 3.14.
The license plate is generally an approximately rectangular or parallelogram-shaped area, the width and height ratio is within a certain range (for example, the standard length-width ratio is 3.14), and the positioned license plate areas are generally not quite different from each other at the ratio.
Therefore, the above embodiment performs positioning adjustment based on the color recognition model and the standard aspect ratio, and the license plate region can be obtained.
And S12, correcting the license plate region to obtain a second image.
In general, due to different photographing angles and photographing conditions, the license plate image may be an angle-skewed picture, so that the license plate image needs to be corrected to avoid affecting the recognition effect.
In at least one embodiment of the present invention, the correcting the license plate region to obtain a second image includes:
Identifying a blue background of the license plate region by using an HSV (Hue, saturation, value) color model;
Performing horizontal expansion processing on the license plate region based on the blue background to obtain a second intermediate image;
performing horizontal differential operation on the second intermediate image to obtain edge information of the second intermediate image;
And rotating the second intermediate image based on the edge information to obtain the second image.
In the above embodiment, the edge information of the license plate region can be highlighted by the horizontal expansion processing, the influence of the character information is removed, the edge detection is further performed by the horizontal differential operation, so as to accurately determine the license plate region, and the second intermediate image is rotated based on the edge information, so that the license plate region is corrected, and the interference of angles in the license plate recognition process is avoided.
And S13, extracting the characteristics of the second image to obtain target characteristics.
In this embodiment, the feature extraction model may be used to perform feature extraction on the second image, and the method of feature extraction is not limited in the present invention.
S14, character recognition is carried out according to the target features, and a license plate recognition result of the license plate image is obtained.
The present embodiment performs character recognition based on a template matching method.
In at least one embodiment of the present invention, the performing character recognition according to the target feature, and obtaining the license plate recognition result of the license plate image includes:
Calling a pre-configured character template dictionary;
calculating the intersection of the target feature and each character template in the character template dictionary to obtain a first feature corresponding to each character template;
performing exclusive OR operation on the first characteristic corresponding to each character template and the target characteristic to obtain a second characteristic corresponding to each character template;
performing exclusive OR operation on the first features corresponding to each character template and each character template to obtain third features corresponding to each character template;
Determining the number of white pixels in each character template as a first number, the number of white pixels in the target feature as a second number, the number of white pixels in the first feature corresponding to each character template as a third number, the number of white pixels in the second feature corresponding to each character template as a fourth number, and the number of white pixels in the third feature corresponding to each character template as a fifth number;
Calculating the average of the first number, the second number and the third number corresponding to each character template to obtain an average value corresponding to each character template;
Calculating the square of the difference value of the first number corresponding to each character template and the average value corresponding to each character template to obtain the first square corresponding to each character template;
calculating the square of the difference value of the second number corresponding to each character template and the average value corresponding to each character template to obtain the second square corresponding to each character template;
Calculating the square of the difference value of the third number corresponding to each character template and the average value corresponding to each character template to obtain the third square corresponding to each character template;
calculating the quotient of the sum of the first square, the second square and the third square corresponding to each character template and a preset value, and calculating the arithmetic square root of the quotient to obtain the square root corresponding to each character template;
calculating the quotient of the fifth number corresponding to each character template and the first number corresponding to each character template to obtain a first quotient corresponding to each character template;
Calculating the quotient of the fourth number corresponding to each character template and the second number corresponding to each character template to obtain a second quotient corresponding to each character template;
Multiplying the square root corresponding to each character template, the first quotient and the second quotient to obtain a product corresponding to each character template;
Calculating the quotient of the product of the third number corresponding to each character template and the product corresponding to each character template to obtain the similarity between the target feature and each character template;
and generating a license plate recognition result of the license plate image according to the characters in the character template with the maximum similarity.
The character template dictionary is used for storing standard character templates so as to match with the features to be identified during character identification.
For example: the target feature is imageU, each character template is imageT, the first feature corresponding to each character template is imageV, the second feature corresponding to each character template is imageX, the third feature corresponding to each character template is imageW, the first number corresponding to each character template is T, the second number corresponding to each character template is U, the third number corresponding to each character template is V, the fourth number corresponding to each character template is X, and the fourth number corresponding to each character template is W, and then the similarity of each character template can be expressed as:
Wherein Q is the average value corresponding to each character template, q= (t+u+v)/3.
And finally, determining the character template corresponding to the maximum Y as the recognition result of each character in the license plate image.
In the embodiment, the recognition of the characters in the license plate image can be realized based on template matching.
In this embodiment, after the license plate recognition result of the license plate image is generated, the license plate recognition result may be encrypted and sent to a designated terminal device, so that relevant staff can check and confirm the license plate recognition result.
It should be noted that, in order to further improve the security of the data and avoid the data from being tampered maliciously, the character template dictionary may be stored in the blockchain node.
According to the technical scheme, the received license plate image can be responded, the license plate image is subjected to brightness-based preprocessing to obtain the first image, brightness compensation is performed on the license plate image, accuracy of recognition due to influence of brightness is avoided, the first image is positioned to obtain a license plate region in the first image, the license plate region is corrected to obtain the second image, the license plate region is corrected, angle interference in the license plate recognition process is avoided, feature extraction is performed on the second image to obtain target features, character recognition is performed according to the target features to obtain a license plate recognition result of the license plate image, further license plate recognition is achieved based on an artificial intelligent means, and recognition efficiency is improved.
Fig. 2 is a functional block diagram of a license plate recognition device based on image processing according to a preferred embodiment of the present invention. The license plate recognition device 11 based on image processing comprises a preprocessing unit 110, a positioning unit 111, a correction unit 112, an extraction unit 113 and a recognition unit 114. The module/unit referred to in the present invention refers to a series of computer program segments capable of being executed by the processor 13 and of performing a fixed function, which are stored in the memory 12. In the present embodiment, the functions of the respective modules/units will be described in detail in the following embodiments.
In response to the received license plate image, the preprocessing unit 110 performs brightness-based preprocessing on the license plate image to obtain a first image.
In this embodiment, the license plate image may be acquired by an image acquisition device (such as a monitoring camera) or may be uploaded by a related staff, which is not limited by the present invention.
In order to facilitate accurate identification of the license plate image, the license plate image needs to be preprocessed.
In at least one embodiment of the present invention, the preprocessing unit 110 performs brightness-based preprocessing on the license plate image, including:
Determining a cut-off of brightness in the license plate image;
dividing the license plate image into a first area and a second area based on the dividing line;
Acquiring a first threshold value corresponding to the brightness in the first area and a second threshold value corresponding to the brightness in the second area which are preset;
Calculating a difference between the second threshold and the first threshold;
When the difference value is larger than a third threshold value, carrying out brightness compensation on the license plate image; or alternatively;
And when the difference value is smaller than or equal to the third threshold value, not performing brightness compensation on the license plate image.
When the difference is greater than the third threshold, it indicates that the luminance distribution of the license plate image is uneven, and there is a larger luminance difference (for example, the luminance of the upper half of the license plate image is darker, and an obvious boundary is formed between the upper half of the license plate image and the lower half of the license plate image), and at this time, if feature extraction is directly performed, features of some regions (for example, darker portions) cannot be extracted, which affects the accuracy of license plate image recognition.
When the difference value is smaller than or equal to the third threshold value, the brightness distribution of the license plate image is uniform, and brightness compensation is not needed.
The third threshold may be configured in a user-defined manner.
Through the embodiment, the brightness compensation can be performed on the license plate image, and the accuracy of identification due to the influence of brightness is avoided.
Specifically, in the first region, the ratio of the number of pixel points with gray values larger than the first threshold to the total number of all pixel points in the first region is a first numerical value; in the second region, the ratio of the number of pixel points with gray values larger than the second threshold to the total number of pixel points in the second region is a second numerical value; wherein the first and second values are values between 0 and 1.
The first value and the second value may also be configured according to actual requirements, which is not limited by the present invention.
Further, after performing brightness compensation on the license plate image, the preprocessing unit 110 performs brightness-based preprocessing on the license plate image further includes:
Sharpening the license plate image subjected to brightness compensation to obtain a first intermediate image;
and carrying out smooth filtering on the first intermediate image to obtain the first image.
Through the embodiment, after brightness compensation, the edge information of the license plate image can be enhanced through sharpening processing, and a complete boundary is formed, so that contour extraction of the license plate image is facilitated. Meanwhile, through smooth filtering, noise of the license plate image can be effectively eliminated.
The positioning unit 111 performs positioning on the first image to obtain a license plate region in the first image.
After the preprocessing, the license plate region can be positioned based on the preprocessed first image.
In at least one embodiment of the present invention, the positioning unit 111 locates the first image, and obtaining the license plate region in the first image includes:
detecting a blue region in the first image using an HSI (Hue-Saturation-INTENSITY (LIGHTNESS)) color model;
detecting a white region in the first image using a RGB (Red Green Blue) model;
and obtaining a standard length-width ratio of the license plate, and adjusting the blue area and the white area according to the standard length-width ratio to obtain the license plate area.
Wherein the standard aspect ratio may be 3.14.
The license plate is generally an approximately rectangular or parallelogram-shaped area, the width and height ratio is within a certain range (for example, the standard length-width ratio is 3.14), and the positioned license plate areas are generally not quite different from each other at the ratio.
Therefore, the above embodiment performs positioning adjustment based on the color recognition model and the standard aspect ratio, and the license plate region can be obtained.
The correction unit 112 corrects the license plate region to obtain a second image.
In general, due to different photographing angles and photographing conditions, the license plate image may be an angle-skewed picture, so that the license plate image needs to be corrected to avoid affecting the recognition effect.
In at least one embodiment of the present invention, the correcting unit 112 corrects the license plate region, to obtain a second image includes:
Identifying a blue background of the license plate region by using an HSV (Hue, saturation, value) color model;
Performing horizontal expansion processing on the license plate region based on the blue background to obtain a second intermediate image;
performing horizontal differential operation on the second intermediate image to obtain edge information of the second intermediate image;
And rotating the second intermediate image based on the edge information to obtain the second image.
In the above embodiment, the edge information of the license plate region can be highlighted by the horizontal expansion processing, the influence of the character information is removed, the edge detection is further performed by the horizontal differential operation, so as to accurately determine the license plate region, and the second intermediate image is rotated based on the edge information, so that the license plate region is corrected, and the interference of angles in the license plate recognition process is avoided.
The extracting unit 113 performs feature extraction on the second image to obtain a target feature.
In this embodiment, the feature extraction model may be used to perform feature extraction on the second image, and the method of feature extraction is not limited in the present invention.
The recognition unit 114 performs character recognition according to the target feature to obtain a license plate recognition result of the license plate image.
The present embodiment performs character recognition based on a template matching method.
In at least one embodiment of the present invention, the recognition unit 114 performs character recognition according to the target feature, and obtaining the license plate recognition result of the license plate image includes:
Calling a pre-configured character template dictionary;
calculating the intersection of the target feature and each character template in the character template dictionary to obtain a first feature corresponding to each character template;
performing exclusive OR operation on the first characteristic corresponding to each character template and the target characteristic to obtain a second characteristic corresponding to each character template;
performing exclusive OR operation on the first features corresponding to each character template and each character template to obtain third features corresponding to each character template;
Determining the number of white pixels in each character template as a first number, the number of white pixels in the target feature as a second number, the number of white pixels in the first feature corresponding to each character template as a third number, the number of white pixels in the second feature corresponding to each character template as a fourth number, and the number of white pixels in the third feature corresponding to each character template as a fifth number;
Calculating the average of the first number, the second number and the third number corresponding to each character template to obtain an average value corresponding to each character template;
Calculating the square of the difference value of the first number corresponding to each character template and the average value corresponding to each character template to obtain the first square corresponding to each character template;
calculating the square of the difference value of the second number corresponding to each character template and the average value corresponding to each character template to obtain the second square corresponding to each character template;
Calculating the square of the difference value of the third number corresponding to each character template and the average value corresponding to each character template to obtain the third square corresponding to each character template;
calculating the quotient of the sum of the first square, the second square and the third square corresponding to each character template and a preset value, and calculating the arithmetic square root of the quotient to obtain the square root corresponding to each character template;
calculating the quotient of the fifth number corresponding to each character template and the first number corresponding to each character template to obtain a first quotient corresponding to each character template;
Calculating the quotient of the fourth number corresponding to each character template and the second number corresponding to each character template to obtain a second quotient corresponding to each character template;
Multiplying the square root corresponding to each character template, the first quotient and the second quotient to obtain a product corresponding to each character template;
Calculating the quotient of the product of the third number corresponding to each character template and the product corresponding to each character template to obtain the similarity between the target feature and each character template;
and generating a license plate recognition result of the license plate image according to the characters in the character template with the maximum similarity.
The character template dictionary is used for storing standard character templates so as to match with the features to be identified during character identification.
For example: the target feature is imageU, each character template is imageT, the first feature corresponding to each character template is imageV, the second feature corresponding to each character template is imageX, the third feature corresponding to each character template is imageW, the first number corresponding to each character template is T, the second number corresponding to each character template is U, the third number corresponding to each character template is V, the fourth number corresponding to each character template is X, and the fourth number corresponding to each character template is W, and then the similarity of each character template can be expressed as:
Wherein Q is the average value corresponding to each character template, q= (t+u+v)/3.
And finally, determining the character template corresponding to the maximum Y as the recognition result of each character in the license plate image.
In the embodiment, the recognition of the characters in the license plate image can be realized based on template matching.
In this embodiment, after the license plate recognition result of the license plate image is generated, the license plate recognition result may be encrypted and sent to a designated terminal device, so that relevant staff can check and confirm the license plate recognition result.
It should be noted that, in order to further improve the security of the data and avoid the data from being tampered maliciously, the character template dictionary may be stored in the blockchain node.
According to the technical scheme, the received license plate image can be responded, the license plate image is subjected to brightness-based preprocessing to obtain the first image, brightness compensation is performed on the license plate image, accuracy of recognition due to influence of brightness is avoided, the first image is positioned to obtain a license plate region in the first image, the license plate region is corrected to obtain the second image, the license plate region is corrected, angle interference in the license plate recognition process is avoided, feature extraction is performed on the second image to obtain target features, character recognition is performed according to the target features to obtain a license plate recognition result of the license plate image, further license plate recognition is achieved based on an artificial intelligent means, and recognition efficiency is improved.
Fig. 3 is a schematic structural diagram of a computer device according to a preferred embodiment of the present invention for implementing a license plate recognition method based on image processing.
The computer device 1 may comprise a memory 12, a processor 13 and a bus, and may further comprise a computer program stored in the memory 12 and executable on the processor 13, such as a license plate recognition program based on image processing.
It will be appreciated by those skilled in the art that the schematic diagram is merely an example of the computer device 1 and does not constitute a limitation of the computer device 1, the computer device 1 may be a bus type structure, a star type structure, the computer device 1 may further comprise more or less other hardware or software than illustrated, or a different arrangement of components, for example, the computer device 1 may further comprise an input-output device, a network access device, etc.
It should be noted that the computer device 1 is only used as an example, and other electronic products that may be present in the present invention or may be present in the future are also included in the scope of the present invention by way of reference.
The memory 12 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 12 may in some embodiments be an internal storage unit of the computer device 1, such as a removable hard disk of the computer device 1. The memory 12 may also be an external storage device of the computer device 1 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the computer device 1. Further, the memory 12 may also include both an internal storage unit and an external storage device of the computer device 1. The memory 12 may be used not only for storing application software installed in the computer device 1 and various types of data, such as codes of license plate recognition programs based on image processing, but also for temporarily storing data that has been output or is to be output.
The processor 13 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, various control chips, and the like. The processor 13 is a Control Unit (Control Unit) of the computer apparatus 1, connects the respective components of the entire computer apparatus 1 using various interfaces and lines, executes various functions of the computer apparatus 1 and processes data by running or executing programs or modules stored in the memory 12 (for example, executing license plate recognition programs based on image processing, etc.), and calls data stored in the memory 12.
The processor 13 executes the operating system of the computer device 1 and various types of applications installed. The processor 13 executes the application program to implement the steps of the respective embodiments of the license plate recognition method based on image processing described above, such as the steps shown in fig. 1.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory 12 and executed by the processor 13 to complete the present invention. The one or more modules/units may be a series of computer readable instruction segments capable of performing the specified functions, which instruction segments describe the execution of the computer program in the computer device 1. For example, the computer program may be divided into a preprocessing unit 110, a positioning unit 111, a correction unit 112, an extraction unit 113, an identification unit 114.
The integrated units implemented in the form of software functional modules described above may be stored in a computer readable storage medium. The software functional module is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a computer device, or a network device, etc.) or a processor (processor) to execute the license plate recognition method based on image processing according to the embodiments of the present invention.
The modules/units integrated in the computer device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on this understanding, the present invention may also be implemented by a computer program for instructing a relevant hardware device to implement all or part of the procedures of the above-mentioned embodiment method, where the computer program may be stored in a computer readable storage medium and the computer program may be executed by a processor to implement the steps of each of the above-mentioned method embodiments.
Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory, or the like.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchain), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The bus may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, only one straight line is shown in fig. 3, but not only one bus or one type of bus. The bus is arranged to enable a connection communication between the memory 12 and at least one processor 13 or the like.
Although not shown, the computer device 1 may further comprise a power source (such as a battery) for powering the various components, preferably the power source may be logically connected to the at least one processor 13 via a power management means, whereby the functions of charge management, discharge management, and power consumption management are achieved by the power management means. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The computer device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described in detail herein.
Further, the computer device 1 may also comprise a network interface, optionally comprising a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the computer device 1 and other computer devices.
The computer device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the computer device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
Fig. 3 shows only a computer device 1 with components 12-13, it being understood by those skilled in the art that the structure shown in fig. 3 is not limiting of the computer device 1 and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
In connection with fig. 1, the memory 12 in the computer device 1 stores a plurality of instructions to implement an image processing based license plate recognition method, the processor 13 being executable to implement:
Responding to a received license plate image, and performing brightness-based preprocessing on the license plate image to obtain a first image;
Positioning the first image to obtain a license plate region in the first image;
correcting the license plate region to obtain a second image;
extracting features of the second image to obtain target features;
and carrying out character recognition according to the target features to obtain a license plate recognition result of the license plate image.
Specifically, the specific implementation method of the above instructions by the processor 13 may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
The data in this case were obtained legally.
In the several embodiments provided in the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The invention is operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. The units or means stated in the invention may also be implemented by one unit or means, either by software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (9)
1. The license plate recognition method based on the image processing is characterized by comprising the following steps of:
Responding to a received license plate image, and performing brightness-based preprocessing on the license plate image to obtain a first image;
Positioning the first image to obtain a license plate region in the first image;
correcting the license plate region to obtain a second image;
extracting features of the second image to obtain target features;
character recognition is carried out according to the target features, and a license plate recognition result of the license plate image is obtained;
the step of carrying out character recognition according to the target features to obtain license plate recognition results of the license plate images comprises the following steps:
Calling a pre-configured character template dictionary;
calculating the intersection of the target feature and each character template in the character template dictionary to obtain a first feature corresponding to each character template;
performing exclusive OR operation on the first characteristic corresponding to each character template and the target characteristic to obtain a second characteristic corresponding to each character template;
performing exclusive OR operation on the first features corresponding to each character template and each character template to obtain third features corresponding to each character template;
Determining the number of white pixels in each character template as a first number, the number of white pixels in the target feature as a second number, the number of white pixels in the first feature corresponding to each character template as a third number, the number of white pixels in the second feature corresponding to each character template as a fourth number, and the number of white pixels in the third feature corresponding to each character template as a fifth number;
Calculating the average of the first number, the second number and the third number corresponding to each character template to obtain an average value corresponding to each character template;
Calculating the square of the difference value of the first number corresponding to each character template and the average value corresponding to each character template to obtain the first square corresponding to each character template;
calculating the square of the difference value of the second number corresponding to each character template and the average value corresponding to each character template to obtain the second square corresponding to each character template;
Calculating the square of the difference value of the third number corresponding to each character template and the average value corresponding to each character template to obtain the third square corresponding to each character template;
calculating the quotient of the sum of the first square, the second square and the third square corresponding to each character template and a preset value, and calculating the arithmetic square root of the quotient to obtain the square root corresponding to each character template;
calculating the quotient of the fifth number corresponding to each character template and the first number corresponding to each character template to obtain a first quotient corresponding to each character template;
Calculating the quotient of the fourth number corresponding to each character template and the second number corresponding to each character template to obtain a second quotient corresponding to each character template;
Multiplying the square root corresponding to each character template, the first quotient and the second quotient to obtain a product corresponding to each character template;
Calculating the quotient of the product of the third number corresponding to each character template and the product corresponding to each character template to obtain the similarity between the target feature and each character template;
and generating a license plate recognition result of the license plate image according to the characters in the character template with the maximum similarity.
2. The image processing-based license plate recognition method according to claim 1, wherein the performing brightness-based preprocessing on the license plate image includes:
Determining a cut-off of brightness in the license plate image;
dividing the license plate image into a first area and a second area based on the dividing line;
Acquiring a first threshold value corresponding to the brightness in the first area and a second threshold value corresponding to the brightness in the second area which are preset;
Calculating a difference between the second threshold and the first threshold;
When the difference value is larger than a third threshold value, carrying out brightness compensation on the license plate image; or alternatively;
And when the difference value is smaller than or equal to the third threshold value, not performing brightness compensation on the license plate image.
3. The image processing-based license plate recognition method according to claim 2, wherein in the first region, a ratio of the number of pixels having a gray value larger than the first threshold value to the total number of all pixels in the first region is a first numerical value; in the second region, the ratio of the number of pixel points with gray values larger than the second threshold to the total number of pixel points in the second region is a second numerical value; wherein the first and second values are values between 0 and 1.
4. The image processing-based license plate recognition method according to claim 2, wherein the performing the brightness-based preprocessing on the license plate image after performing the brightness compensation on the license plate image further comprises:
Sharpening the license plate image subjected to brightness compensation to obtain a first intermediate image;
and carrying out smooth filtering on the first intermediate image to obtain the first image.
5. The license plate recognition method based on image processing as claimed in claim 1, wherein the positioning the first image to obtain a license plate region in the first image includes:
detecting a blue region in the first image using an HSI color model;
Detecting a white region in the first image using an RGB model;
and obtaining a standard length-width ratio of the license plate, and adjusting the blue area and the white area according to the standard length-width ratio to obtain the license plate area.
6. The license plate recognition method based on image processing as claimed in claim 1, wherein the correcting the license plate region to obtain a second image includes:
identifying a blue background of the license plate region by using an HSV color model;
Performing horizontal expansion processing on the license plate region based on the blue background to obtain a second intermediate image;
performing horizontal differential operation on the second intermediate image to obtain edge information of the second intermediate image;
And rotating the second intermediate image based on the edge information to obtain the second image.
7. The utility model provides a license plate recognition device based on image processing which characterized in that, license plate recognition device based on image processing includes:
the preprocessing unit is used for responding to the received license plate image, preprocessing the license plate image based on brightness and obtaining a first image;
the positioning unit is used for positioning the first image to obtain a license plate region in the first image;
The correction unit is used for correcting the license plate region to obtain a second image;
The extraction unit is used for extracting the characteristics of the second image to obtain target characteristics;
The recognition unit is used for carrying out character recognition according to the target characteristics to obtain a license plate recognition result of the license plate image;
The identification unit includes:
Calling a pre-configured character template dictionary;
calculating the intersection of the target feature and each character template in the character template dictionary to obtain a first feature corresponding to each character template;
performing exclusive OR operation on the first characteristic corresponding to each character template and the target characteristic to obtain a second characteristic corresponding to each character template;
performing exclusive OR operation on the first features corresponding to each character template and each character template to obtain third features corresponding to each character template;
Determining the number of white pixels in each character template as a first number, the number of white pixels in the target feature as a second number, the number of white pixels in the first feature corresponding to each character template as a third number, the number of white pixels in the second feature corresponding to each character template as a fourth number, and the number of white pixels in the third feature corresponding to each character template as a fifth number;
Calculating the average of the first number, the second number and the third number corresponding to each character template to obtain an average value corresponding to each character template;
Calculating the square of the difference value of the first number corresponding to each character template and the average value corresponding to each character template to obtain the first square corresponding to each character template;
calculating the square of the difference value of the second number corresponding to each character template and the average value corresponding to each character template to obtain the second square corresponding to each character template;
Calculating the square of the difference value of the third number corresponding to each character template and the average value corresponding to each character template to obtain the third square corresponding to each character template;
calculating the quotient of the sum of the first square, the second square and the third square corresponding to each character template and a preset value, and calculating the arithmetic square root of the quotient to obtain the square root corresponding to each character template;
calculating the quotient of the fifth number corresponding to each character template and the first number corresponding to each character template to obtain a first quotient corresponding to each character template;
Calculating the quotient of the fourth number corresponding to each character template and the second number corresponding to each character template to obtain a second quotient corresponding to each character template;
Multiplying the square root corresponding to each character template, the first quotient and the second quotient to obtain a product corresponding to each character template;
Calculating the quotient of the product of the third number corresponding to each character template and the product corresponding to each character template to obtain the similarity between the target feature and each character template;
and generating a license plate recognition result of the license plate image according to the characters in the character template with the maximum similarity.
8. A computer device, the computer device comprising:
a memory storing at least one instruction; and
A processor executing instructions stored in the memory to implement the image processing-based license plate recognition method according to any one of claims 1 to 6.
9. A computer-readable storage medium, characterized by: the computer-readable storage medium has stored therein at least one instruction that is executed by a processor in a computer device to implement the image processing-based license plate recognition method of any one of claims 1 to 6.
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| CN109145915A (en) * | 2018-07-27 | 2019-01-04 | 武汉科技大学 | License plate rapid distortion antidote under a kind of complex scene |
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| CN109145915A (en) * | 2018-07-27 | 2019-01-04 | 武汉科技大学 | License plate rapid distortion antidote under a kind of complex scene |
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