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Coal dust image identification method

A technology of image recognition and dust, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of low accuracy of segmentation algorithm and difficulty in fitting particles with more than 3 overlaps by clustering algorithm

Active Publication Date: 2016-12-07
XIAN UNIV OF SCI & TECH
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Problems solved by technology

Obviously, the application of edge tracking algorithm cannot well solve the problem of coal dust particle identification. Although the method of mathematical morphology is not limited by the shape of the analyzed object, the accuracy of its segmentation algorithm is low; Particles; while the area outline obtained by the watershed transform algorithm has airtightness, connectivity, single pixel width and precise position, but the watershed algorithm has the problem of over-segmentation

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Embodiment Construction

[0101] Such as figure 1 As shown, the coal dust image recognition method of the present invention comprises the following steps:

[0102] Step 1. Segment the coal dust image using fuzzy rough sets based on multi-attribute reduction. The specific process is as follows:

[0103] Step 101. Determination of fuzzy category membership: the image processor uses the acquired coal dust image as a fuzzy rough set Y={y 1 ,y 2 ,...,y n′} to deal with, in the fuzzy rough set Y={y 1 ,y 2 ,...,y n′} to construct k′ clusters m 1 ,m 2 ,...,m k′ , and determine y i′ corresponds to w i′ The degree of membership of the fuzzy category

[0104] Among them, y i′ is the gray value of the i'th pixel in the coal dust image, i'=1, 2,...,n', n' is the number of pixels, k' is a non-zero natural number, w i′ is the pixel in the domain U of fuzzy rough set;

[0105] During specific implementation, the image of coal dust processed by the image processor is obtained by using a microscope magni...

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Abstract

The invention discloses a coal dust image identification method. The method comprises steps that 1, a fuzzy-rough set based on multi-attribute reduction is adopted to segment a coal dust image; 2, an image processor calls a binary image processing module to carry out binarization processing of the coal dust image, a coal dust binary image is obtained, and a target area in the coal dust binary image is labeled as 1 and a background area in the coal dust binary image is labeled as 0; and 3, an improved differential evolutionary particle swarm optimization algorithm is adopted to carry out coal dust overlap particle separation of the coal dust image, and coal dust particles are identified. The steps are simple, the design is novel and reasonable, the implementation is convenient, the precision of coal dust overlap particle identification is improved, the effectiveness and the robustness are good, the adaptability is strong, the usage is flexible and convenient, the practicality is strong, the use effect is good, and the popularization and application value is high.

Description

technical field [0001] The invention belongs to the technical field of coal dust image processing, and in particular relates to a coal dust image recognition method. Background technique [0002] In coal preparation plants with severe coal dust pollution, during the screening, crushing and transportation of raw coal, due to the volatilization and drying of coal moisture, a large amount of dust will be generated in the process of being vibrated, impacted and caused to fall, and the concentration of coal dust will reach a certain level. When combined with oxygen in the presence of an open flame, a vicious safety accident of coal dust explosion will occur at any time, causing great harm. And too much coal dust will cause serious wear and tear on expensive and sophisticated equipment and instruments, causing the aging of the machine, reducing the service life of precision instruments, and also causing the problem of pneumoconiosis among workers. Therefore, it is necessary to ac...

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Application Information

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IPC IPC(8): G06K9/00G06K9/34G06K9/40
CPCG06V20/695G06V10/267G06V10/30
Inventor 王征
Owner XIAN UNIV OF SCI & TECH
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