License plate image illumination processing method based on evaluation feedback system
A license plate image and feedback system technology, applied in the research field of license plate recognition, can solve the problem that the license plate number is difficult to be recognized by human eyes and computers.
Active Publication Date: 2015-03-25
SUN YAT SEN UNIV
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[0003] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, and provide a license plate image illumination processing method based on an evaluation feedback system, which effectively solves the problem that the license plate number is difficult to be recognized by human eyes and computers in a complex illumination environment. problem, thereby improving the recognition rate of the license plate number
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[0053] Such as figure 1 , figure 2 As shown, the license plate image illumination processing method based on the evaluation feedback system specifically includes the following steps:
[0054] (1) Convert the input license plate image from the RGB model to the HSV model;
[0055] (2) Perform a series of lighting preprocessing methods on the V component to cope with complex lighting environments, hereinafter referred to as the TT method. This method first uses Gamma correction to enhance the image brightness, and then performs Gaussian difference filtering (DOG, Difference of Gaussian) to remove the high-frequency components of the illumination, that is, the uneven part, and then extracts the area that needs further processing according to the mask function, and finally adopts contrast equalization Enhance the contrast of image illumination, so as to remove the uneven illumination while retaining the useful details of the image to the greatest extent and enhance the image con...
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The invention discloses a license plate image illumination processing method based on an evaluation feedback system. The method comprises the following steps that (1) an input license plate image is converted into an HSV model from an RGB model; (2) a series of illumination processing operations are carried out on a V component; (3) local self-adaptive threshold processing is carried out on the enhanced license plate image to obtain a license plate binary image; (4) the license plate character condition in the license plate binary image is evaluated; (5) whether the condition of stopping feedback is met or not is judged; (6) whether selection of human eye observation is needed or not is judged, if selection of human eye observation is needed, the license plate binary image needs to be skeletonized, then the license plate binary image is adopted as a seed point to carry out a region growing method on the enhanced license plate image, the pixel value of a growing region is improved, and if selection of human eye observation is not needed, license plate characters are directly recognized, and a computer recognition result is output. The problem that license plate numbers are hard for human eyes and computers to recognize under complex illumination environment is effectively solved, and therefore the recognition rate of the license plate numbers is improved.
Description
technical field [0001] The invention relates to the research field of license plate recognition, in particular to a license plate image illumination processing method based on an evaluation feedback system. Background technique [0002] As an indispensable and important part of intelligent transportation system, license plate number recognition has gradually developed into the main means of vehicle identification and is widely used in the fields of monitoring and alarming, punishment for violations, access management and highway toll management. In the past ten years, although the license plate recognition technology has made great progress, in some practical applications, many factors such as illumination, angle, resolution and license plate type affect the recognition rate to varying degrees, among which illumination is the most common. a disturbing factor. At present, lighting processing methods are roughly divided into three categories: learning-based methods, feature-b...
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IPC IPC(8): G06K9/00G06K9/36
CPCG06V20/584G06V10/30G06V20/625
Inventor 许跃生张永东谭利
Owner SUN YAT SEN UNIV