阅读说明：本技术 一种昏暗环境下矿车车牌识别的方法 (Method for recognizing license plate of mine car in dark environment ) 是由 曲伟霞 侯成录 张汉阔 潘伟 郑飞 许芝光 徐婧 王秋玲 邓帅 冯瑞军 侯令超 于 2021-07-02 设计创作，主要内容包括：本发明公开了一种昏暗环境下矿车车牌识别的方法,包括以下步骤：第一步：图像采集；第二步：预处理；第三步：车牌定位；第四步：字符分割；第五步：字符识别；第六步：结果输出。本发明中通过预处理,解决了昏暗光线下的亮度过低、图像噪声过多的问题,并采用多种算法结合定位车牌的方式,避免了昏暗环境下车牌的错误定位,最终再结合多种分割、识别算法,解决了井下无法准确识别车牌的问题。(The invention discloses a method for identifying a license plate of a mine car in a dark environment, which comprises the following steps: the first step is as follows: collecting an image; the second step is that: pre-treating; the third step: positioning a license plate; the fourth step: character segmentation; the fifth step: character recognition; and a sixth step: and outputting the result. According to the invention, through preprocessing, the problems of too low brightness and too much image noise under dim light are solved, the wrong positioning of the license plate under the dim environment is avoided by adopting a mode of combining multiple algorithms and positioning the license plate, and finally, the problem that the license plate cannot be accurately identified underground is solved by combining multiple segmentation and identification algorithms.)

1. A method for identifying a license plate of a mine car in a dark environment comprises the following steps: the method comprises the following steps of firstly, acquiring an image, and is characterized by further comprising the following steps:

the second step is that: preprocessing, including backlight, overexposure and noisy image processing on the collected graph;

the third step: positioning a license plate;

the fourth step: character segmentation;

the fifth step: character recognition;

and a sixth step: and outputting the result.

2. The method for recognizing the license plate of the mine car in the dim environment according to claim 1, wherein the method comprises the following steps: in the first step, images are collected in a video mode, a vehicle head or a vehicle tail is detected by using an RCNN model during collection, and when the vehicle head or the vehicle tail is detected, the images are intercepted.

3. The method for recognizing the license plate of the mine car in the dim environment according to claim 1, wherein the method comprises the following steps: the second step is to perform automatic exposure processing, automatic white balance processing, automatic backlight processing and automatic overexposure processing according to the field environment and the image acquired in the first step, and perform noise filtering, contrast enhancement and image scaling processing on the image.

4. The method for recognizing the license plate of the mine car in the dim environment according to claim 3, wherein the method comprises the following steps: the noise filtering method is mean filtering, median filtering or Gaussian filtering; the contrast enhancement method is contrast linear stretching, histogram equalization or using a homomorphic filter; the image scaling processing method is a nearest neighbor interpolation method, a bilinear interpolation method or a cubic convolution interpolation method.

5. The method for recognizing the license plate of the mine car in the dim environment according to claim 1, wherein the method comprises the following steps: and thirdly, detecting the position of the license plate in the image by respectively adopting a projection analysis method, a connected domain analysis method and a machine learning algorithm, judging that the license plate exists in the current image if the license plate is detected in the current image by the three methods and the difference values of the position coordinates of the three acquired license plates are all within a preset threshold value, and cutting the license plate from the image.

6. The method for recognizing the license plate of the mine car in the dim environment according to claim 5, wherein the method comprises the following steps:

the projection analysis method is that the license plate is positioned by the projection analysis of the image in the horizontal and vertical directions according to the times of the alternate appearance of the characters of the license plate and the background;

the connected domain analysis method is characterized in that the license plate is positioned by detecting and combining the connected domains according to the characteristic that each character in the license plate is a connected domain and the structures and colors of the connected domains are consistent;

the machine learning algorithm is that firstly, a weak classifier is trained into a strong classifier by using features extracted from a plurality of license plate samples, and then the strong classifier is used for scanning and detecting images, so that the license plates are positioned.

7. The method for recognizing the license plate of the mine car in the dim environment according to claim 5, wherein the method comprises the following steps: and thirdly, after the license plate is cut from the image, correcting the cut license plate image.

8. The method for recognizing the license plate of the mine car in the dim environment according to claim 7, wherein the method comprises the following steps: the license plate image is corrected by a Hough transformation method, a rotating projection method, a variance minimization method, a perspective transformation method or a principal component analysis method;

the Hough transformation method is used for calculating the inclination angle by detecting straight lines of the upper frame, the lower frame, the left frame and the right frame of the license plate;

the rotation projection method is to vertically project an image on a horizontal axis according to different angles, and the angle when the sum of the points with the projection value of 0 is maximum is taken as a vertical inclination angle;

the variance minimization method is a closed expression of a vertical inclination angle derived according to the minimum coordinate variance of a projection point of a character in the vertical direction, so that the vertical inclination angle is determined;

the perspective transformation method is to realize the distortion correction of the license plate by utilizing four vertexes of the detected license plate after the four vertexes are transformed by a correlation matrix;

the principal component analysis method is characterized in that the principal component direction of the color to the characteristic point is calculated according to the characteristic that the color at the boundary of the license plate background and the characters is fixedly matched, and then the horizontal inclination angle of the license plate is obtained.

9. The method for recognizing the license plate of the mine car in the dim environment according to claim 1, wherein the method comprises the following steps: in the fourth step, the character segmentation step is as follows: based on the binarization result or the edge extraction result of the license plate, the structural features of characters, the similarity between the characters and the interval information between the characters are utilized, on one hand, single characters are respectively extracted, the adhered and broken characters are processed during extraction, on the other hand, the characters with similar width and height are classified into one class, and thus the license plate frame and noise are removed.

10. The method for recognizing the license plate of the mine car in the dim environment according to any one of claims 1 to 9, wherein: in the fifth step, the algorithm of character recognition adopts a template matching method, an artificial neural network method, a support vector machine method or an Adaboost classification method.

Technical Field

The invention relates to a license plate recognition method, in particular to a method for recognizing a license plate of a mine car under an underground dim environment.

Background

The traditional underground mine car management mainly depends on manual license plate recording and then determines whether to pass according to the authority. The mode has low management efficiency and high labor cost, and cannot meet the management requirements of mines on vehicle transportation amount and vehicle transportation scale.

With the development of the field of artificial intelligence vision, the vision method of the license plate is gradually applied. However, the existing license plate recognition methods are all directed at recognition under the condition of good light, the underground draw shaft feeding port is dim in light, and the license plate is often shielded by dust and other impurities, so that the license plate at the tail part of the vehicle cannot be accurately recognized by adopting the existing recognition methods.

Disclosure of Invention

The invention provides a method for identifying a license plate of a mine car in a dark environment, which aims to: and finishing the recognition of the license plate in a dark environment.

The technical scheme of the invention is as follows:

a method for identifying a license plate of a mine car in a dark environment comprises the following steps:

the method comprises the following steps of firstly, image acquisition:

the second step is that: preprocessing, including backlight, overexposure and noisy image processing on the collected graph;

the third step: positioning a license plate;

the fourth step: character segmentation;

the fifth step: character recognition;

and a sixth step: and outputting the result.

As a further improvement of the identification method: in the first step, images are collected in a video mode, a vehicle head or a vehicle tail is detected by using an RCNN model during collection, and when the vehicle head or the vehicle tail is detected, the images are intercepted.

As a further improvement of the identification method: the second step is to perform automatic exposure processing, automatic white balance processing, automatic backlight processing and automatic overexposure processing according to the field environment and the image acquired in the first step, and perform noise filtering, contrast enhancement and image scaling processing on the image.

As a further improvement of the identification method: the noise filtering method is mean filtering, median filtering or Gaussian filtering; the contrast enhancement method is contrast linear stretching, histogram equalization or using a homomorphic filter; the image scaling processing method is a nearest neighbor interpolation method, a bilinear interpolation method or a cubic convolution interpolation method.

As a further improvement of the identification method: and thirdly, detecting the position of the license plate in the image by respectively adopting a projection analysis method, a connected domain analysis method and a machine learning algorithm, judging that the license plate exists in the current image if the license plate is detected in the current image by the three methods and the difference values of the position coordinates of the three acquired license plates are all within a preset threshold value, and cutting the license plate from the image.

As a further improvement of the identification method: the projection analysis method is that the license plate is positioned by the projection analysis of the image in the horizontal and vertical directions according to the times of the alternate appearance of the characters of the license plate and the background;

the connected domain analysis method is characterized in that the license plate is positioned by detecting and combining the connected domains according to the characteristic that each character in the license plate is a connected domain and the structures and colors of the connected domains are consistent;

the machine learning algorithm is that firstly, a weak classifier is trained into a strong classifier by using features extracted from a plurality of license plate samples, and then the strong classifier is used for scanning and detecting images, so that the license plates are positioned.

As a further improvement of the identification method: and thirdly, after the license plate is cut from the image, correcting the cut license plate image.

As a further improvement of the identification method: the license plate image is corrected by a Hough transformation method, a rotating projection method, a variance minimization method, a perspective transformation method or a principal component analysis method;

the Hough transformation method is used for calculating the inclination angle by detecting straight lines of the upper frame, the lower frame, the left frame and the right frame of the license plate;

the rotation projection method is to vertically project an image on a horizontal axis according to different angles, and the angle when the sum of the points with the projection value of 0 is maximum is taken as a vertical inclination angle;

the variance minimization method is a closed expression of a vertical inclination angle derived according to the minimum coordinate variance of a projection point of a character in the vertical direction, so that the vertical inclination angle is determined;

the perspective transformation method is to realize the distortion correction of the license plate by utilizing four vertexes of the detected license plate after the four vertexes are transformed by a correlation matrix;

the principal component analysis method is characterized in that the principal component direction of the color to the characteristic point is calculated according to the characteristic that the color at the boundary of the license plate background and the characters is fixedly matched, and then the horizontal inclination angle of the license plate is obtained.

As a further improvement of the identification method: in the fourth step, the character segmentation step is as follows: based on the binarization result or the edge extraction result of the license plate, the structural features of characters, the similarity between the characters and the interval information between the characters are utilized, on one hand, single characters are respectively extracted, the adhered and broken characters are processed during extraction, on the other hand, the characters with similar width and height are classified into one class, and thus the license plate frame and noise are removed.

As a further improvement of the identification method: in the fifth step, the algorithm of character recognition adopts a template matching method, an artificial neural network method, a support vector machine method or an Adaboost classification method.

Compared with the prior art, the invention has the following beneficial effects:

(1) according to the method, through optimization preprocessing, automatic exposure processing, automatic white balance processing, automatic backlight processing, automatic overexposure processing and the like of a camera are carried out according to analysis of a field environment and a shot image, and processing such as noise filtering, contrast enhancement, image scaling and the like is carried out on the image, so that a foundation is laid for accurate recognition of a license plate in a dim environment.

(2) The method adopts three different algorithms of a projection analysis method, a connected domain analysis method and a machine learning algorithm to detect the position of the license plate in the image, only when the license plate is detected in the current image by the three methods and the difference value between the position coordinates of the three acquired license plates is within a preset threshold value, the license plate in the current image is judged to exist, and the license plate is cut out from the image based on the obtained coordinates, so that the method can avoid the error positioning of the license plate caused by the larger influence on the image in the previous preprocessing process.

(3) The optional range of the character recognition algorithm is large, the collocation is flexible, the template matching method can achieve the effects of high recognition speed and simple method, the artificial neural network method has the advantages of strong learning ability, strong adaptability, strong classification ability and the like, the support vector machine method has better recognition ability for unseen test samples and needs less training samples, the Adaboost classification method can focus on more important training data, and the recognition speed is high and the real-time performance is high.

Drawings

FIG. 1 is a flow chart of the method of the present invention.

Detailed Description

The technical scheme of the invention is explained in detail in the following with the accompanying drawings:

referring to fig. 1, a method for identifying a license plate of a mine car in a dark environment comprises the following steps:

the method comprises the following steps of firstly, collecting an image. The method comprises the steps of installing a camera at a barrier where a mine car passes through, collecting images in a video mode, detecting the head or tail of the car by using a pre-trained RCNN model during collection, and intercepting the images to wait for further processing when the head or tail of the car is detected.

The second step is that: and (4) preprocessing. Because the image quality is easily influenced by factors such as illumination, weather and camera position, the camera and the image need to be preprocessed before the license plate is identified, so that the clearest image of the license plate is ensured to be obtained.

The steps comprise backlight, overexposure and noisy image processing on the collected images, and a foundation is laid for accurate recognition of the license plate in a dark environment. Specifically, according to the field environment and the image obtained in the first step, automatic exposure processing, automatic white balance processing, automatic backlight processing and automatic overexposure processing are performed, and noise filtering, contrast enhancement and image scaling processing are performed on the image.

The noise filtering method is mean filtering, median filtering or Gaussian filtering; the contrast enhancement method is contrast linear stretching, histogram equalization or using a homomorphic filter; the image scaling processing method is a nearest neighbor interpolation method, a bilinear interpolation method or a cubic convolution interpolation method.

The third step: and (5) positioning the license plate.

In the step, according to the texture feature, the color feature and the shape feature information, a projection analysis method, a connected domain analysis method and a machine learning algorithm are respectively adopted to detect the position of the license plate in the image, if the license plate is detected in the current image by the three methods and the difference value between the three acquired license plate position coordinates is within a preset threshold value, the license plate in the current image is judged to exist, and the license plate is cut out from the image.

The method can avoid the error positioning of the license plate caused by the larger influence on the image in the previous preprocessing process.

Specifically, the method comprises the following steps:

the projection analysis method is that the license plate is positioned by the projection analysis of the image in the horizontal and vertical directions according to the times of the alternate appearance of the characters of the license plate and the background;

the connected domain analysis method is characterized in that the license plate is positioned by detecting and combining the connected domains according to the characteristic that each character in the license plate is a connected domain and the structures and colors of the connected domains are consistent;

the machine learning algorithm is that firstly, a weak classifier is trained into a strong classifier by using features extracted from a plurality of license plate samples, and then the strong classifier is used for scanning and detecting images, so that the license plates are positioned.

Further, due to the influence of factors such as a shooting angle and a lens, a license plate in an image has deformation such as horizontal inclination, vertical inclination or trapezoidal distortion, which brings difficulty to subsequent recognition processing. After the license plate is positioned, license plate correction processing is firstly carried out, so that noise such as a license plate frame is favorably removed, and character recognition is more favorably carried out. Therefore, after the license plate is cut from the image, the cut license plate image is corrected.

The license plate image is corrected by a Hough transformation method, a rotation projection method, a variance minimization method, a perspective transformation method or a principal component analysis method:

the Hough transformation method is used for calculating the inclination angle by detecting straight lines of the upper frame, the lower frame, the left frame and the right frame of the license plate;

the rotation projection method is to vertically project an image on a horizontal axis according to different angles, and the angle when the sum of the points with the projection value of 0 is maximum is taken as a vertical inclination angle;

the variance minimization method is a closed expression of a vertical inclination angle derived according to the minimum coordinate variance of a projection point of a character in the vertical direction, so that the vertical inclination angle is determined;

the perspective transformation method is to realize the distortion correction of the license plate by utilizing four vertexes of the detected license plate after the four vertexes are transformed by a correlation matrix;

the principal component analysis method is characterized in that the principal component direction of the color to the characteristic point is calculated according to the characteristic that the color at the boundary of the license plate background and the characters is fixedly matched, and then the horizontal inclination angle of the license plate is obtained.

The fourth step: and (5) character segmentation. After the license plate area is located, since the information of a plurality of characters, the position relation among the characters, the width and the height of each character and the like in the license plate is not known, the character segmentation is an essential step for ensuring the license plate type matching and the correct character recognition.

The method comprises the following specific steps: based on the binarization result or the edge extraction result of the license plate, the structural features of characters, the similarity between the characters and the interval information between the characters are utilized, on one hand, single characters are respectively extracted, the adhered and broken characters are processed during extraction, on the other hand, the characters with similar width and height are classified into one class, and thus the license plate frame and noise are removed.

The character segmentation can specifically adopt a connected domain analysis method, a projection analysis method, character clustering or a template matching method.

The fifth step: and (3) character recognition, wherein a template matching method, an artificial neural network method, a support vector machine method or an Adaboost classification method is adopted in the step.

The template matching method has the advantages of high identification speed and simplicity, and has the defects of difficult treatment on the conditions of fracture, fouling and the like; the artificial neural network method has strong learning ability, strong adaptability and strong classification ability but is time-consuming; the support vector machine method has better identification capability for unseen test samples and needs less training samples; the Adaboost classification method can focus on more important training data, and is high in recognition speed and high in real-time performance. The license plate in China is composed of 3 characters, namely Chinese characters, English letters and Arabic numerals, has a uniform pattern, and is a convenient point in the identification process. However, because the license plate is easily affected by the external environment, the situations of blurring, breaking and smearing characters occur, and how to improve the recognition rate of the characters and the characters which are easy to be confused is also one of the difficulties in character recognition. Confusing characters include: 0 and D, 0 and Q, 2 and Z, 8 and B, 5 and S, 6 and G, 4 and A, etc.

And a sixth step: and outputting the result.

Furthermore, due to the complex image background on site and the unclear positioning of the license plate, noise such as fences, billboards and the like can be easily used as the license plate. In order to improve the positioning accuracy and the recognition speed, the license plate recognition system is provided with an external interface, so that a user can set different recognition areas according to the field environment.