License plate detection method based on deep learning

A license plate detection and deep learning technology, applied in the field of license plate detection based on deep learning, can solve the problems of not meeting the real-time requirements of the system, prone to missed detection, and high missed detection rate, meeting the real-time requirements and low missed detection rate. , improve the detection effect

Inactive Publication Date: 2016-10-12
CHENGDU XINEDGE TECH
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However, this method still has a big problem. If the number of image pyramid layers is too small, the amount of calculation is small, but the missed detection rate is high. If the number of image pyramid layers is too large, the missed detection rate is low, but th

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  • License plate detection method based on deep learning
  • License plate detection method based on deep learning
  • License plate detection method based on deep learning

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[0026] The present invention will be further explained below in conjunction with the drawings:

[0027] The present invention will be further explained below in conjunction with the drawings:

[0028] Some terms in the present invention are explained as follows:

[0029] Term 1: faster-rcnn algorithm

[0030] The faster-rcnn algorithm includes an RPN convolutional neural network and a fast-rcnn convolutional neural network: the RPN convolutional neural network is aimed at regression problems, and is responsible for obtaining the coarse selection area of ​​the license plate in the present invention; fast-rcnn convolutional neural network The network is aimed at the problem of discrimination, and in the present invention, it is responsible for further screening the rough selection area of ​​the obtained license plate.

[0031] Term 2: BP algorithm

[0032] BP algorithm is a back-propagation algorithm, divided into two parts: forward process and reverse process. The forward process refers ...

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Abstract

The invention discloses a license plate detection method based on deep learning, comprising the following steps: using a fast-rcnn algorithm to train an RPN convolution neural network and a fast-rcnn convolution neural network; building an image library with marks and tags as a sample set; using the trained RPN convolution neural network to process the images in the sample set to get a rough license plate area; sending a rough license plate box to the trained fast-rcnn convolution neural network for judgment; and judging whether the rough license plate area is an optimal license plate area according to the output vector of the fast-rcnn convolution neural network, and if the rough license plate area is an optimal license plate area, taking the rough license plate area as a final license plate area. According to the invention, a multi-scale and multi-proportion reference box is adopted in training of the RPN convolution neural network, so detection of license plates with unconventional scale and proportion is promoted effectively. The RPN convolution neural network and the fast-rcnn convolution neural network share convolution layer parameters, so the whole system is simpler, less amount of calculation is needed, and the rate of missed detection is lower. Moreover, the real-time requirement of the system can be satisfied.

Description

technical field [0001] The invention belongs to the technical field of computer vision recognition, and in particular relates to a license plate detection method based on deep learning. Background technique [0002] Computer vision is an important interdisciplinary subject in the fields of artificial intelligence and image processing. Early solutions to computer vision tasks mainly consisted of two steps, one was to manually design features, and the other was to build a shallow learning system. With the development of artificial intelligence, deep learning was formally proposed in 2006. Deep learning originated from multi-layer artificial neural networks, and has been successfully applied in fields such as computer vision, natural language processing, and intelligent search. Currently existing deep learning networks mainly include convolutional neural networks, deep belief networks, and stacked autoencoders. Convolutional neural network is widely used in image processing ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/02
CPCG06N3/02G06V20/00G06V20/625G06F18/214
Inventor 邹刚蒋涛李鸿升
Owner CHENGDU XINEDGE TECH
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