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Construction method and device and recognition method and device of deep learning recognition model for inclined license plate

A deep learning and recognition model technology, applied in neural learning methods, character and pattern recognition, biological neural network models, etc., can solve the problem of low recognition accuracy

Active Publication Date: 2019-07-16
WUHAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] In view of this, the present invention provides a method for constructing a deep learning recognition model for inclined license plates, a recognition method and a device to solve or at least partially solve the technical problem of low recognition accuracy in the methods in the prior art

Method used

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  • Construction method and device and recognition method and device of deep learning recognition model for inclined license plate
  • Construction method and device and recognition method and device of deep learning recognition model for inclined license plate
  • Construction method and device and recognition method and device of deep learning recognition model for inclined license plate

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Embodiment 1

[0057] The applicant of the present invention has found through a large amount of research and practice that the existing convolutional neural network has poor effects and inaccurate results in processing oblique images, so it proposes a recognition neural network framework and its construction for license plates under oblique conditions method. Specifically, the idea of ​​spatial transformation network is used.

[0058] Spatial Transformer Networks (STN) has a good processing effect on pictures in special forms. The license plate under the tilted condition can be considered as the license plate in the normal form after a one-step affine transformation. STN can be used to transform the The tilted license plate image is converted to a license plate image in normal form. The main idea of ​​the invention is as follows: the four vertex coordinates of the license plate are obtained mainly by predicting the affine parameters of the license plate, and then the feature maps of differ...

Embodiment 2

[0085] This embodiment provides a device for constructing a deep learning recognition model for tilted license plates, please refer to Figure 7 , the device consists of:

[0086] The training data set construction module 201 is used to collect tilted license plate images, construct a training data set, record the license plate number of each tilted license plate image, and mark the license plate coordinates in each tilted license plate image, wherein the license plate coordinates include four vertices Actual coordinates, according to the preset virtual coordinates and actual coordinates of the four vertices, calculate the corresponding affine parameters;

[0087] The training data set division module 202 is used to divide the training data set into a license plate location training set and a license plate recognition training set according to corresponding affine parameters and license plate numbers;

[0088] The deep learning recognition model framework construction module ...

Embodiment 3

[0104] This embodiment provides a recognition method for inclined license plates, the method comprising:

[0105] Input the license plate image to be recognized into the trained deep learning recognition model constructed in Embodiment 1 to obtain the recognition result.

[0106] Specifically, see Figure 8 , is the schematic diagram of the recognition method for inclined license plates.

[0107] Specifically, obtaining the recognition result specifically includes:

[0108] Predict the affine parameters of the license plate through the positioning network of the trained deep learning recognition model, and calculate the real coordinates of the license plate to be recognized according to the preset virtual coordinates of the license plate and the predicted affine parameters;

[0109] Through the recognition network of the trained deep learning recognition model, the license plate number is recognized according to the calculated real coordinates of the license plate to be reco...

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Abstract

The invention discloses a construction method and device and a recognition method and a recognition device for a deep learning recognition model of an inclined license plate. The construction method comprises the steps: determining a license plate coordinate from a collected license plate image, and calculating an affine parameter; constructing a deep learning network framework for identifying theinclined license plate; and training a positioning network by using the collected data set, and training a license plate character recognition network through the trained parameter model and the trained license plate data set. The invention provides a recognition network framework based on a deep learning method for inclined license plate recognition, and the technical effect of greatly improvingthe recognition precision of the inclined license plate can be achieved.

Description

technical field [0001] The invention relates to the field of computer application technology, in particular to a construction method, recognition method and device for a deep learning recognition model for inclined license plates. Background technique [0002] With the rapid growth of urban population, the number of vehicles owned by urban residents has increased rapidly, and the management of vehicles in urban traffic has become more and more complicated. Intelligent license plate recognition has emerged as the times require. License plate recognition technology can help solve complex urban traffic management problems to a great extent. Automatic license plate recognition has the characteristics of high recognition rate, fast recognition speed, full license plate support, and complete recognition functions. It can assist traffic control departments to make up for the shortcomings of slow manual recognition, inaccurate recognition, and omissions in recognition. With the acc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/32G06N3/04G06N3/08
CPCG06N3/08G06V30/1478G06V20/63G06V20/625G06N3/045
Inventor 章登义张强武小平章辉宇
Owner WUHAN UNIV
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