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End-to-end lightweight deep license plate recognition method

A license plate recognition, lightweight technology, applied in the field of image processing technology and deep learning

Active Publication Date: 2020-07-10
NANJING UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, it also brings challenges to the management of vehicles. In many parking places and social security systems, license plate recognition is required.

Method used

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  • End-to-end lightweight deep license plate recognition method
  • End-to-end lightweight deep license plate recognition method
  • End-to-end lightweight deep license plate recognition method

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Embodiment Construction

[0091] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0092] In one embodiment, combined with figure 1 , provides an end-to-end lightweight deep license plate recognition method, which includes the following steps:

[0093] Step 1, collect the image containing the license plate and construct the license plate data set;

[0094] Step 2, build a license plate detection network;

[0095] Step 3, obtaining the image of the license plate area;

[0096] Step 4, build a license plate recognition network;

[0097] Step 5, consider the license plate detection network and the license plate recognition network as a whole, and perfor...

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Abstract

The invention discloses an end-to-end lightweight deep license plate recognition method, and belongs to the field of image processing and deep learning. The method comprises the steps of collecting animage containing a license plate, and constructing a license plate data set; constructing a license plate detection network; acquiring a license plate area image; constructing a license plate recognition network; regarding the license plate detection network and the license plate recognition network as a whole, and performing end-to-end training on the whole network; and recognizing the license plate in the to-be-detected license plate image by using the trained network. According to the method, end-to-end training is carried out on the model, the calculated amount and time of the license plate recognition process are reduced through the lightweight convolutional neural network, and the method has the advantages of being low in requirement for the shooting angle of the to-be-recognized image, accurate in license plate positioning, high in license plate character recognition accuracy, high in recognition speed and the like.

Description

technical field [0001] The invention belongs to the field of image processing technology and deep learning, specifically relates to the field of target recognition, in particular to an end-to-end lightweight deep license plate recognition method. Background technique [0002] With the rapid advancement of technology, vehicles have become popular as a means of transportation. However, it also brings challenges to the management of vehicles. In many parking places and social security systems, license plate recognition is required. At the same time, with the development of the field of computer vision, the human requirements brought about by vehicle recognition are gradually being replaced by machines. [0003] The general license plate recognition process includes four steps: license plate location, license plate correction, character segmentation, and character recognition. These steps are usually implemented through multiple deep learning models. For example, the license p...

Claims

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Application Information

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IPC IPC(8): G06K9/32G06K9/46G06K9/62G06N3/04
CPCG06V10/255G06V10/44G06V20/625G06N3/045G06F18/253G06F18/214
Inventor 张伟张浩茅耀斌韩翊项文波曹倩倩卓一
Owner NANJING UNIV OF SCI & TECH
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