Selective search and convolutional neural network based vehicle logo recognition method

A convolutional neural network and recognition method technology, applied in character and pattern recognition, instruments, computer components, etc., can solve problems such as high dimensionality, poor real-time performance, long calculation time, etc., and achieve high recognition rate and robustness , to achieve the effect of convenience and strong adaptability

Inactive Publication Date: 2016-08-17
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

Method used

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  • Selective search and convolutional neural network based vehicle logo recognition method
  • Selective search and convolutional neural network based vehicle logo recognition method
  • Selective search and convolutional neural network based vehicle logo recognition method

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Embodiment Construction

[0034] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0035] refer to figure 1 , the steps that the present invention realizes are as follows:

[0036] Step 1, input the picture of the vehicle logo to be detected taken by the high-definition camera equipment at the traffic intersection.

[0037] The picture of the car logo to be detected is a picture that contains a clearly visible car logo facing the front or rear of the car. The pixel size of the picture is 500×500, such as figure 2 (a) shown.

[0038] Step 2, use selective search to obtain candidate regions.

[0039] (2a) Based on the graph segmentation of the graph, the initialized region R is obtained:

[0040] (2a1) The photo is abstractly represented by a weighted graph, where the weighted graph is composed of a node set V and an edge set E, expressed as G=(V,E), and the node set V={v 1 ,v 2 ,...,v i ,...,v n}, where i∈[1,n], n is the number of n...

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Abstract

The invention proposes a selective search and convolutional neural network based vehicle logo recognition method, and mainly solves the problems of complicated calculation and poor timeliness in the prior art. According to the implementation scheme, the method comprises the steps of 1) inputting a to-be-detected picture shot by a high-definition shooting device in a traffic intersection; 2) carrying out selective search for the to-be-detected picture to obtain candidate regions; 3) screening the candidate regions to obtain vehicle logo candidate regions; and 4) constructing and training a convolutional neural network (CNN) and inputting the logo candidate regions into the trained CNN for testing to obtain a vehicle logo recognition result. According to the method, the calculation amount is effectively reduced, the vehicle logo candidate regions can be quickly obtained, a self-learning characteristic of the CNN has higher robustness for environmental change, and the vehicle logo recognition rate is increased; and the method can be used for quick detection of freeway entrances and parking spaces to vehicles.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a vehicle logo recognition method, which can be used for rapid detection of vehicles at expressway entrances and parking lots. 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 fields of highway entrance, unmanned parking lot management, and automatic recording of illegal vehicles. Its realization has great economic value and practical significance. [0003] Vehicle logo recognition is an important aspect of vehicle identification. Vehicle logo recognition technology r...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/32
CPCG06V10/245G06V2201/09G06F18/24147
Inventor 韩红程素华张鼎衣亚男何兰江津
Owner XIDIAN UNIV
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