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Instrument pointer jittering recognition method based on support vector machine during instrument detection

A technology of support vector machine and instrument pointer, applied in character and pattern recognition, instruments, computer parts and other directions, can solve the problems of inaccurate recognition accuracy, pointer error, etc., to achieve short recognition time, improve accuracy, Improve real-time effect

Inactive Publication Date: 2014-08-27
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that in the actual industrial production, the accuracy of the meter always decreases with the increase of the use time, and the continuous shaking of the pointer caused by the defect of the meter itself brings errors to the industrial process. The way to identify the jitter of the meter pointer has high inaccuracy and insufficient recognition accuracy, and a method for recognizing the jitter of the meter pointer based on the support vector machine in meter detection is proposed

Method used

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  • Instrument pointer jittering recognition method based on support vector machine during instrument detection
  • Instrument pointer jittering recognition method based on support vector machine during instrument detection
  • Instrument pointer jittering recognition method based on support vector machine during instrument detection

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specific Embodiment approach 1

[0024] Specific Embodiment 1: A recognition method based on a support vector machine-based instrument pointer shake in instrument detection in this embodiment is specifically prepared according to the following steps:

[0025] Step 1. Divide the detected instrument types into various instrument subcategories;

[0026] Step 2. Obtain a clear picture of the instrument panel under normal conditions as a training sample before testing each instrument subcategory;

[0027] Step 3, preprocessing the training sample; wherein, the preprocessing refers to zooming the training sample image and using a noise suppression filter to perform denoising processing; if it is a grayscale image, then binarize the grayscale image ; Common noises include salt and pepper noise and Gaussian noise. The characteristic of salt and pepper noise is that the location of the noise is random, but the amplitude of the noise is basically the same; the characteristic of Gaussian noise is that the location is ce...

specific Embodiment approach 2

[0033] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that in step 4, feature extraction is performed on the preprocessed image to obtain the feature space formed by each feature value of the image as follows:

[0034] (1) The extracted color, grayscale, contour and position feature values, extracting contour features to determine the position of the pointer vertex and the width of the pointer;

[0035] (2) Normalize the feature space formed by the extracted eigenvalues ​​in Matlab; normalization can facilitate subsequent data processing, and speed up the convergence of the program when running, preventing network training caused by the existence of singular sample data Time increases and may cause the network to fail to converge; the normalized feature space is the training model. Other steps and parameters are the same as those in Embodiment 1.

specific Embodiment approach 3

[0036] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is: in step five, according to the image feature space obtained in step four, the SVM classifier is used for training, and the specific process of generating the SVM training model of the training set is:

[0037] (1) The SVM model improves the generalization ability of the learning machine by seeking the minimum structural risk, and realizes the minimization of the empirical risk and the confidence range, so as to achieve the purpose of obtaining good statistical laws even when the statistical sample size is small; The goal of using the training data set to train the SVM model is to determine a dividing line to completely separate the two types of training samples with the label -1 and the label 1 in the training data set, so as to distinguish whether the pixel in the image is located between the instrument pointer Middle; the boundary line has different forms for differ...

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Abstract

The invention discloses an instrument pointer jittering recognition method based on a support vector machine during interment detection. The instrument pointer jittering recognition method based on the support vector machine during interment detection solves the problems that due to the facts that the accuracy degree of an instrument is decreased and the instrument jitters, errors are caused for the industry, the exertion time of a control variable is long, the magnitude is not accurate, and an existing mode is not accurate or not enough in recognition accuracy. The method comprises the steps of (1) distinguishing subclasses of the instrument, (2) obtaining a training sample, (3) preprocessing the training sample, (4) obtaining a feature space, (5) generating a training model, (6) obtaining the optimal SVM training model, (7) recognizing and segmenting an image to be tested, (8) determining whether an instrument pointer jitters or not, and the like. The instrument pointer jittering recognition method is applied to the field of jittering of the instrument pointer.

Description

technical field [0001] The invention relates to a method for recognizing instrument pointer shaking based on a support vector machine in instrument detection. Background technique [0002] In the industrial production process, the use of instruments can be seen everywhere. Instruments are mainly used to detect, display, record or control various industrial parameters. Instruments are necessary instruments and basic means for controlling the industrial production process. Only by knowing the operation of the whole process at all times and performing corresponding controls can the production be safe and smooth and achieve the corresponding goals. However, in actual industrial production, the accuracy of the instrument always decreases with the increase of the use time, and the continuous shaking of the pointer caused by the defect of the instrument itself sometimes occurs. The error caused by the jitter of the instrument pointer has a great impact on a strict industrial proce...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06T7/00
Inventor 尹珅武放王光高会军
Owner HARBIN INST OF TECH
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