The Research of Automatic License Plate Location Algorithm Under Various Conditions

• 1.    ntroduction

• 2.    Pretreatment of License Plate Image

The vehicle license plate recognition (VLPR) is one important research task of the computer vision and mode recognition technology in the intelligent transportation system (ITS), and it has been widely applied in many aspects such as intelligent traffic control, traffic accident automatic measurement, electric charge, vehicle checking and tracing [1]. It is the important utilization of the image processing technology, the mode recognition, the artificial intelligent technology and the automatic control technology in the modern traffic management, and it utilizes the character that each legal vehicle has unique license plate to pick up the information of license plate to manage vehicles. And it needs not to install bar code and wireless receiving equipment, which can avoid large change of the existing traffic system. As the first approach of the license plate recognition, the vehicle license plate location plays a very important function in the license plate recognition system and is the most difficult problem in the license plate recognition system. The license plate location system is based on the digital image processing technology, and can finally confirm and separate the position of the license plate from the image through a series of processing of the license plate image shot by the camera, and then prepare for subsequent license plate recognition. Chinese vehicle license plates are made

* Corresponding author.

according to the Motor Vehicle Plate Standard GA36-92 of China of 1992, and the plate includes 7 characters, and the background colors and character colors are different for different vehicle types [1], so the color information can not be used directly for the initial location of license plate. In addition, the license plate not only contains characters and numbers, but also Chinese character (the first character of the license plate), and the hanging positions of the license plate are different, and the motors with dirty license plates can run on the road, and the images of autos usually have complex backgrounds such as billboards, trees, buildings, other running vehicles and foot passengers, and zebra crossings, which bring certain difficulty for the exact location of auto license plates.

The rest of this paper is organized as follows. Section II describe the image preprocessing technology of license plate.Section Ш describe the method of positioning plate based on the horizontal and vertical projections. The method has the adaptive capacity of complex background.Finally, section V offers concluding remarks.

The target of the image pretreatment is to give prominence to the information of object, and weaken unnecessary information and interference noises. The pretreatment process mainly includes the steps from the step (2) to the step (7) in Figure 1. After acquire the image of vehicle, for the unification and convenience of subsequent processing, translate the image format into the 24 bit true color bitmap of 640×480.

(7)                         (6)

Fig 1. Flow of Vehicle License Plate Location

• A.    Grey scale

By analyzing the color distribution and characters of familiar license plate in China, following conclusions can be obtained [2].

• (1)    There are five colors together in China, i.e. yellow, black, blue, white, and red.

• (2)    There are five sorts of color combination of front ground and background, such as the blue-bottom whitecharacter white-frame line of small automobile license plate, and the yellow-bottom black-character blackframe line of teacher-automobile.

• (3)    Except for the license plates of large automobiles and the automobiles of temporary entry, the size of the license plate is 440mm×140mm.

Based on above characters, many researchers use the information of color to locate the area of license plate, such as the color edge algorithm, and the distance and similarity algorithm [3], but these position methods can not effectively eliminate the interference of the background colors such as the advertisements on the auto, and the lines of the automobiles, and in addition, these methods are too sensitive for the illumination, so the application is limited. This article uses the grey image to locate the license plate, which can reduce the subsequent computation and quicken the speed of location. The grey image still can reflect the distribution and character of the chroma and brightness class of total image. There are many methods of grey scale of the image, such as the averaging method, the weighted averaging method, and the maximization method [4]. The weighted averaging method is fit for the visual character of human eyes, and the effect is relatively better. For the convenience of implementation, the weighted averaging method is adopted in the article. Use g to denote the grey value of pixel point after grey scale, and R, G and B respectively denote the red component, the green component, and the blue component in the original true color image, so g = 0.322R + 0.588G + 0.11 B.

• B.    Contrast enhancement

  The images of license plate images shot under different illumination conditions are largely different, and the contrast of some automobile images is deficient, which makes the characters of license plate can not be distinguished and positioned, so the effective method must be adopted to enhance the contrast of image. There are many feasible methods such as the grey degree conversion, the linear filter, and the column diagram

  
  

  modification. Though many experiments, a grey conversion algorithm enhancing the self-adaptive contrast is designed in the article according to the characters of the license plate image.

  
  

  Suppose that the original grey degree value of certain one point

  
  

  ( x , y )

  
  

  in the image is

  
  

  f ( x , y ) , and after

  
  

  the self-adaptive grey degree conversion, the green degree value is values in the grey degree conversion, so

  
  

  ^{g ( x , y )} , ^fh and ^fl

  
  

  are two threshold

  
  
  
  

  g( x , У ) <  fl

  
  

  g ( x , У ) = ’

  
  

  ( f ( x , У ) - f l )*

  
  

  f ( x , y ) fh. — f

  
  

  f <  g ( x , У ) <  fh

  
  
  
  
  
  

g⁽ x , У) <  fi

The cause that sets up two extreme

grey degree values in the class of grey degree as 0 and 255 in the grey degree conversion is that two extremes of the license plate image are either the white-bottom black-character or the black-bottom white-character.

• C.    License plate image noise processing

To restrain the noises introduced in the shoot and pretreatment process of the license plate image, the filtering processing is necessary. When the low-pass filtering eliminates the noise, it makes the edge of the license plate thickened, which makes against the subsequent processing, and the high-pass filtering can strengthen not only the edge of the license plate, but also the noises, so the method of mean-value filtering is adopted to eliminate the noises. The approaches to compute the mean-value of window pixel taking the point (x, y) as the center can be described as follows.

• (1)    Rank the pixel points in the window according to the grey degree values.

• (2)    Select the mean-value in the ranking pixel set as the new value of the point (x, y).

• D.    Edge detection

The vehicle image has obvious edge information, so the Sobel operator can be utilized to test the edge of the vehicle before the binaryzation. The representative edge test operators include Laplace operator and the Sobel operator. The Laplace operator is the second-order differential operator with large computation quantity, which is very sensitive to the noise, and it is not fit to detect the edge of the license plate image under variable illumination conditions. Comparing with other two operators, the Sobel operator has two prominent advantages, (1) because of the introduction of average factor, it possesses certain smoothing function for the random noise in the image, (2) because it is the difference being at a distance of two rows or two columns, so the elements at both sides of the edge are strengthened, which makes the edge to look coarse and bright. By the test, the Sobel vertical edge operator could better restrain other edge information of vehicles and give prominence to the character edge information in the area of license plate, so the vertical Sobel operator is used in the article to test the vertical edges of the license plate image.

• E.    Binaryztion and mathematical morphology processing

  The binaryzation can largely eliminate the interferences of road surfaces and vehicle body to the license plate location and the quality of the license plate binaryzation directly influences the effect of the license plate location (He, 2009). The improved binaryzation algorithm of complete threshold value is adopted to implement the binaryzation processing for the license plate image, and this algorithm has good self-adaptive ability under complex background. If the grey degree class range of one image f ( x . y ) is ( ¹ , ^K ) , suppose T is one

ZZ number between 1 and K , the optimal threshold value can be solved by the iterative method, i.e. the threshold value T. The concrete approaches of this algorithm can be described as follows.

(1)Determine the minimum grey degree value Z and the maximum grey degree value Z in the image, and suppose the initial value of the threshold value is т 0 = Z1 + Z г 2

(2)Divide the image into two parts including target and background according to the threshold value T^K , and determine the average grey values Z A and Z B of two parts.

Z   ^{z (} i , ^j ) ^x N ⁽ i , ^j )

_{7  =} z (i, j ) < TK _____________________

A =       Z   N ( i , j )

z ( i, j ) < T K

Z   z ⁽ i , j ) ^x N ⁽ i , j )

• 7  _ z ( i , j ) > TK _____________________

B "      Z N ( i , j )

z ( i , j ) > T K

Where, Z (i, j) is the grey value of the point ( i, j ) in the image, and N (i, j) is the amount of the pixel point which grey degree value is Z (i, j) in the image.

• (3)    Determine the new threshold value.

j K +1 _ ^Z A + ZB