Fabric flatness objective evaluation method and fabric flatness objective evaluation device based on unsupervised machine learning

Abstract

An embodiment of the invention discloses a fabric flatness objective evaluation method and a fabric flatness objective evaluation device on unsupervised machine learning, wherein the method comprises the steps of acquiring sample data in a standard evaluation environment; preprocessing the acquired sample data, eliminating background information and interference information of an image; vectorizing the preprocessed data by means of computer image processing technology; classifying the vectorized data, and generating a characteristic reference set; and performing image class prediction on the characteristic reference set, thereby obtaining an evaluation result. In the fabric flatness objective evaluation method and the fabric flatness objective evaluation device, through extracting and abstracting a bottom-layer characteristic, a fabric image is vectorized; clustering is performed according to the characteristic of the fabric image, and a label is set for a clustering result. Through unified extraction and abstraction on the bottom layer characteristic and objective reference classification, fabric grade prediction is performed, thereby obtaining an evaluation result in a more fair and objective manner, reducing an error caused by artificial adoption of data for training, and furthermore preventing a subjective error caused by artificial evaluation.

Description

Technical field [0001] The invention relates to the technical field of image processing, in particular to a method and device for evaluating the flatness of fabrics based on unsupervised machine learning. Background technique [0002] Textiles and garments are affected by many factors in the process of household daily care and industrial washing, and their appearance changes, which reduces the appearance and wearability of clothes. People's requirements for clothing are not only the appearance style, but also its anti-wrinkle ability. Therefore, when the factory produces fabrics, it also needs to conduct related tests on its anti-wrinkle ability. Therefore, the American Association of Textile Chemists and Dyers has formulated relevant standards (such as AATCC 124) to classify different wrinkles. However, the traditional rating is done manually, due to the influence of work intensity, pressure and other factors, the result of the assessment has a certain degree of subjectivity. ...

AI Technical Summary

Problems solved by technology

In the process of manual evaluation, due to the large amount of collected data, the evaluation results are often different due to changes in the environment. The subjective factors are very large, and it is time-consuming and labor-intensive. The final data is full of many uncertain factors.

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