Textile qualitative classification method

Abstract

The invention provides a textile qualitative classification method, and belongs to the field of textile identification. The textile qualitative classification method comprises the following steps: (1)establishing a qualitative classification prediction model by using a convolutional network; (2) collecting a near infrared spectrum of a textile sample to be detected, and processing the collected near infrared spectrum to obtain a processed near infrared spectrum; and (3) inputting the processed near infrared spectrum into a qualitative classification prediction model, wherein the qualitative classification prediction model outputs the category of the textile sample to be detected. According to the invention, the normalized and pixelated near infrared spectrum is adopted, so that the acquisition difficulty and time are saved, and the method is an environment-friendly and rapid detection method; according to the method, the network weight and the offset value are automatically obtained through convolution kernel training, the spectral characteristics can be automatically extracted, the adaptability is high, the automatic qualitative classification problem of cotton, polyester and other textiles is effectively solved, and the detection level and speed of textile components are effectively improved.

Description

technical field [0001] The invention belongs to the field of textile identification, and in particular relates to a qualitative classification method for textiles. Background technique [0002] With the development of textile industry and garment industry and the improvement of people's living standards, people's requirements for textile fabrics are also getting higher and higher. In order to meet people's needs, there are more and more novel types of fabrics on the market. The fiber composition and content of fabrics is one of the main indicators to determine its commodity value, and it is also an item that has attracted great attention from consumers. At the same time, a large number of waste textiles are also facing the problem of sorting by category in the recycling process. Therefore, accurate and automatic qualitative or quantitative analysis of fiber composition is becoming more and more important in textile testing, whether it is at the consumer level or in the rec...

AI Technical Summary

Problems solved by technology

However, the extraction of waveform features is still a difficult classification problem, and it is difficult to achieve high accuracy by using peak detection or Fourier transform.

[0004] The recycling of waste textiles has increasingly become a growth point for the sustainable and gre

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