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Textile flaw detecting method

A defect detection and textile technology, which is applied in the field of textile defect detection for periodic pattern textiles, can solve the problems of poor detection effect, high cost, and long detection method cycle, achieve good detection accuracy, improve detection rate, and improve MRF model method. Effect

Inactive Publication Date: 2018-11-06
CHANGZHOU COLLEGE OF INFORMATION TECH
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Problems solved by technology

[0006] In view of the above situation, in order to solve the problems of long detection period, high cost and poor detection effect of the detection method in the prior art, the present invention proposes a textile defect detection method based on the double-layer MRF model, by determining the size of the basic repeating unit Crop and block the image, which reduces the computational complexity and improves the detection rate. At the same time, this method is universal to the types of defects

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[0035] The present invention will be further described in detail below with reference to the embodiments given in the accompanying drawings. The described embodiments include various specific details to aid in understanding, but they are to be regarded only as exemplary, some, not all, embodiments of the invention. Unless otherwise defined, the technical terms or scientific terms used herein shall have the usual meanings understood by those skilled in the art to which the present invention belongs. Meanwhile, detailed descriptions of functions and constructions well-known in the art will be omitted for clarity and conciseness of the description.

[0036] A textile defect detection method that applies a two-layer MRF model, such as figure 1As shown, the present invention adopts a double-layer Markov random field model to detect defects in periodic textile images, and the model is divided into a top-layer constrained field and a bottom-layer data field. Among them, the purpose...

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Abstract

A textile flaw detecting method comprises the following steps: inputting a to-be-detected textile image containing periodic variation patterns to serve as a to-be-detected sample image; determining the size of a template of a basic repeating unit; carrying out shearing and blocking to the to-be-detected sample image according to the determined size of the template, and building a top layer restraint field in a double-layer MRF(markov random field) model; carrying out bilinear interpolation on the to-be-detected sample image, and taking the expanded image as a bottom layer data field in the double-layer MRF model; calculating image energy of each image block; carrying out iterative solution on the double-layer MRF model through EM (expectation-maxi-mization) algorithm, and ensuring that allimage energies reach stable image markers; determining flaw positions according to a final image marking field, and outputting final detecting results. Blocking to the sample image is performed by seeking the size of the smallest repeating unit, the detecting speed is improved, moreover, a traditional MRF model method is improved. Therefore, the textile flaw detecting method provided by the invention has relatively better detecting accuracy.

Description

technical field [0001] The invention relates to the technical field of textile detection, in particular to a textile defect detection method for periodic pattern textiles. Background technique [0002] Textile defect detection is an important part of fabric quality control and an important application in the field of computer vision. Textile defect detection mainly faces the following problems: the time consumed by detection, the success rate and accuracy of detection, and the evaluation standard of detection, etc. Due to industrial production, today's textiles can be divided into two categories: the first category is textiles without periodic patterns; the second category is textiles with periodic patterns. [0003] The first type of textiles appeared earlier, usually in solid colors and with a relatively simple structure. For the defect detection of this type of textiles, many mature algorithms have been developed, which can be roughly divided into the following categori...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/88G01N21/956
CPCG01N21/8851G01N21/956G01N2021/888G01N2021/8887
Inventor 常兴治朱川胡丽英刘威梁久祯侯振杰崔瑶瑶
Owner CHANGZHOU COLLEGE OF INFORMATION TECH
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