Cotton fault detecting and identifying method based on multi-spectrum technology

Abstract

The invention discloses a cotton fault detecting and identifying method based on a multi-spectrum technology. The method comprises the steps: (1) image acquiring; (2) image pre-processing; (3) the preliminary detection of faults; carrying out edge detection on an improved morphological gradient algorithm to obtain the comprehensive edge detection intensities of all faults to obtain an image with contrast enhancement of all faults; (4) precise location of faults: converting the image into a connected domain image through an improved iterative threshold and morphological processing, precisely locating the faults by selecting a main fault algorithm, and extracting fault areas by marking; (5) cotton ginning quality judgment: carrying out statistics on the amount of all faults, and calculating the total cotton faults to obtain a cotton ginning quality grade. The cotton fault detecting and identifying method provided by the invention has the advantages of being simple in theory, fast in detection speed and high in detection precision.

Description

technical field [0001] The invention belongs to the technical field of optical measurement, in particular to an automatic classification detection and identification method for cotton gin quality. Background technique [0002] Lint defects refer to two categories: impurities with fibers and fibers that hinder weaving, including seven types of broken seeds, sterile seeds, wires, soft seed skins, stiff flakes, fibrous seed crumbs, and neps. Since the defects are difficult to discharge during the weaving process, the residual impurities are wrapped in the yarn sliver or attached to the surface of the yarn, resulting in deterioration of the evenness, increase of neps and impurities in the yarn, and increase of the hairiness of the yarn. The surface of the gray cloth made of yarn with a large number of fiber seed crumbs shows a large number of neps and impurities. After dyeing, the cloth surface will appear infertile color spots and hair grains, and the hand feels stiff and rough...

AI Technical Summary

Problems solved by technology

[0004] (1) Existing technologies and methods generally use visible light as the light source, which can only effectively identify some defects, and the quality discrimination accuracy is low

[0005] (2) There are many image interference factors

Due to the fluctuation characteristics of the light source and various interferences such as shadows, light reflection and absorption in the imaging process under industrial conditions, even pure white cotton may have uneven brightness and darkness in the imaging results, making general detection methods vulnerable to interference factors Impact

[0006] (3) There are seven types of cotton defects with complex shapes, which increases the difficulty of detection

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