Automobile wheel hub classification method based on word bag model and support vector machine

A technology of support vector machines and automobile hubs, which is applied in computer parts, character and pattern recognition, instruments, etc., can solve the problems of manual completion, time-consuming and laborious, and achieve the effect of high classification accuracy.

Inactive Publication Date: 2017-04-19
YANGZHOU XIQI AUTOMATION TECH
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Problems solved by technology

At present, these tasks are mainly done manually, which is laborious and

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  • Automobile wheel hub classification method based on word bag model and support vector machine
  • Automobile wheel hub classification method based on word bag model and support vector machine
  • Automobile wheel hub classification method based on word bag model and support vector machine

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Embodiment Construction

[0018] The present invention will be specifically described below in conjunction with the accompanying drawings; the described implementation examples are only for the purpose of illustration, rather than limiting the scope of the present invention.

[0019] The present invention proposes a method for detecting and classifying automobile wheels based on bag-of-words model and extreme learning machine. The machine classifier recognizes any car wheel as a certain type of car wheel.

[0020] figure 1 is a flowchart of the present invention. refer to figure 1 , the present invention realizes steps as follows:

[0021] Step 1. Preprocess the given car wheel image, and use manual calibration to make it a standard image for training and testing.

[0022] Step 2. Extract global features

[0023] A. Color Feature Analysis of Automobile Wheel Image

[0024] The project intends to analyze the red, green, and blue channels of the car wheel image separately, and study the difference...

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Abstract

The invention discloses an automobile wheel hub classification method based on a word bag model and a support vector machine. The automobile wheel hub classification method includes the steps of 1) pretreating a given automobile wheel hub images to make the given automobile wheel hub images become a standard database for training and test; 2) extracting global texture characteristics and global color characteristics of images, 3) extracting partial characteristics of the images employing SIFT descriptors, conducting K-Means clustering for the partial characteristics to form characteristic description based on the word bag model, 4) fusing global and partial characteristics, using an extreme learning machine to learn and obtain an automobile wheel hub classifier; and 5) testing samples to be tested based on the obtained automobile wheel hub classifier to obtain final classification result. Global and partial characteristics are fused, and an extreme learning machine is used to learn an automobile wheel hub classifier. More accurate classification performance can be obtained than a conventional automobile wheel hub classification method based on KNN, SVM and BP. The accuracy in most tests reaches over 99.9%.

Description

technical field [0001] The invention relates to the field of detection and classification of automobile hubs, in particular to the on-line detection and classification of different automobile hubs on a production line by using monocular vision. Background technique [0002] In the automobile industry, people need to classify the detection and classification of automobile wheels, so as to provide decision-making for the next process. At present, these tasks are mainly done manually, which is labor-intensive. There is no way to operate with vision and artificial intelligence. The present invention seeks to address this need. Contents of the invention [0003] The technical problem solved by the present invention is to propose a car wheel detection and classification method based on bag-of-words model and extreme learning machine, and fully utilize global texture features, global color features, global shape features and local features based on bag-of-words model to describ...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2411G06F18/214
Inventor 胡凯翁理国夏旻
Owner YANGZHOU XIQI AUTOMATION TECH
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