License plate recognition method based on trusted area

A license plate recognition and area technology, applied in the field of license plate recognition based on trusted areas, can solve problems such as ineffective detection, and achieve the effect of improving accuracy

Active Publication Date: 2018-10-19
NANCHANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The first is that it cannot effectively detect smaller license plates
Second, billboard

Method used

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  • License plate recognition method based on trusted area
  • License plate recognition method based on trusted area
  • License plate recognition method based on trusted area

Examples

Experimental program
Comparison scheme
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Embodiment Construction

[0058] The present invention is described in detail below with reference to accompanying drawing and embodiment:

[0059] attached Figure 1-8 It can be seen that a license plate recognition method based on trusted regions,

[0060] Use k-means++ clustering to select the number and scale of the initial license plate candidate frame;

[0061] The YOLO-L model first distinguishes between vehicles and other objects on the road;

[0062] The license plate recognition algorithm judges whether the license plate area is located in the vehicle area, so as to eliminate the wrong recognition of the license plate area;

[0063] If the license plate area is located in these vehicle areas, it is considered that the license plate area is detected correctly, and the license plate recognition is completed.

[0064] The YOLO-L model divides the input image into S×S grids;

[0065] If the center of an object falls into a grid cell, that grid cell is responsible for detecting the object;

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Abstract

The invention relates to a license plate recognition method based on a trusted area. K-means++ clustering is carried out to select the number and scales of initial license plate candidate frames; theobtained number and scales of initial candidate frames are combined to a YOLO-L model, so that the positioning accuracy of the vehicle area and license plate area is improved. With the YOLO-L model, avehicle area and a license plate area are located and coordinates of an upper left corner and a lower right corner are outputted; vehicles and other objects on road are distinguished by the YOLO-L model; whether the license plate area is in the vehicle area is determined based on a license plate recognition algorithm, thereby eliminating false identification of the license plate area; and if thelicense plate area is located in the vehicle area, the license plate area is detected correctly to complete license plate identification. According to the license plate recognition method provided bythe invention, the license plate and the similar objects are distinguished well; the license plate is located effectively; the misjudgment of the license plate is reduced; the misjudgment of similar objects is reduced substantially; the cense plate recognition efficiency is improved; and the accuracy of license plate recognition is enhanced.

Description

technical field [0001] The invention relates to a license plate recognition method, in particular to a license plate recognition method based on a trusted region. Background technique [0002] Intelligent Transportation Systems (ITS) play an important role in traffic measurement and monitoring, for example, tracking stolen cars, controlling access to parking lots and limited traffic zones, and collecting traffic flow statistics. The license plate recognition method consists of four steps, namely image capture, license plate area localization, character segmentation and character recognition. The license plate area location step is to detect and extract the rectangular license plate area from the image. The character segmentation step refers to separating the characters on the license plate area. The character recognition step is to convert the image-based characters into text expressions. The license plate area location is the key process of ITS. Its positioning accuracy di...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/584G06V20/625G06F18/23213
Inventor 闵卫东李祥鹏廖艳秋刘瑞康
Owner NANCHANG UNIV
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