Convolutional neural network based vehicle logo identification method

A technology of convolutional neural network and recognition method, which is applied in the direction of neural learning method, biological neural network model, character and pattern recognition, etc. It can solve the problems of increasing computational complexity, long computing time, and high dimensionality, so as to reduce the amount of computing and complexity, improved recognition rate, and high robustness

Inactive Publication Date: 2016-02-24
XIDIAN UNIV
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Problems solved by technology

The disadvantages of this method are: First, because this method uses the HOG feature of the directional gradient histogram, the HOG descriptor generation process of the directional gradient histogram is lengthy, resulting in slow speed and poor real-time performance.
The disadvantage of this method is that in the positioning process, the strong classifier Adaboost algorithm based on the Haar feature and the support vector machine SVM algorithm based on the HOG

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  • Convolutional neural network based vehicle logo identification method
  • Convolutional neural network based vehicle logo identification method
  • Convolutional neural network based vehicle logo identification method

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Embodiment Construction

[0032] The present invention will be further described below in conjunction with the accompanying drawings.

[0033] refer to figure 1 , the concrete steps that the present invention realizes are as follows:

[0034] Step 1, input the picture to be detected taken by the high-definition camera equipment at the traffic intersection.

[0035] The image to be detected contains clearly visible license plates and logos, and the standard license plate area size is 180×60 pixels.

[0036] Step 2, car logo positioning.

[0037] Perform a binarization operation on the input image to be detected to obtain a binarized image. The specific operation is:

[0038] The first step is to select the three primary colors red, green and blue RGB values ​​of the license plate background color in 50 mark samples, and count the mean value of the three primary colors red, green and blue RGB values;

[0039] In the second step, according to the following formula, the binarization operation is perfor...

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Abstract

The invention provides a convolutional neural network based vehicle logo identification method. The method is specifically implemented by the following steps of: (1) inputting a to-be-detected picture shot by a high-resolution camera device in a traffic intersection; (2) positioning a vehicle logo; (3) constructing and training a convolutional neural network; and (4) identifying the vehicle logo. With the adoption of the convolutional neural network (CNN) based vehicle logo identification method, the shortcomings of complicated extraction feature operator, poor timeliness and complicated model in the prior art can be effectively overcome, and the calculation amount is effectively reduced; and features of CNN self-learning have higher robustness on environmental change, so that the identification rate of the vehicle logo is increased.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a vehicle logo recognition method based on a convolutional neural network in the technical field of image-based pattern recognition. In the traffic system, the invention uses the vehicle pictures obtained by the high-definition camera equipment installed at the intersection to locate the vehicle logo, and then recognize the vehicle logo, so as to realize the automatic positioning and recognition of the vehicle logo. Background technique [0002] With the continuous improvement of the socio-economic level and the popularization of vehicles, the ever-expanding transportation industry has a greater demand for more intelligent technologies and systems, and intelligent transportation systems have become a hot issue in social life. As an important part of the intelligent transportation system, the vehicle identification system has a wide range of applications in the fiel...

Claims

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Application Information

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IPC IPC(8): G06K9/32G06N3/08
CPCG06N3/084G06V10/245G06V20/625
Inventor 韩红焦李成张鼎王伟叶旭庆李阳阳马文萍王爽
Owner XIDIAN UNIV
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