Fabric weft inclination rapid-detection method based on machine vision

Abstract

The invention discloses a fabric weft inclination rapid-detection method based on machine vision. The method comprises the following steps that a CCD video camera is adopted to collect texture images of fabrics, the best segmentation threshold value T is automatically found by using the Otsu method, image target and background segmentation is conducted on the texture images of the fabrics according to the best segmentation threshold value T, and binary texture images of the fabrics are obtained; the stripe trend in the binary texture images of the fabrics is tracked in a window pixel searching method, isolated point noise is removed, a stripe tracking starting point and end point are determined, and finally the stripe weft inclination angle is obtained; a plurality of stripes are tracked by adopting a stripe space domain retraining method, a set of stripe weft inclination angle values are obtained, selective averaging is conducted on the stripe weft inclination angle values, and final fabric stripe image weft inclination angle values are obtained. The method effectively avoids influence on weft inclination angle detection of environment light, is high in fabric weft inclination angle detection accuracy, and can rapidly detect the fabric weft inclination angle in real time.

Description

technical field [0001] The invention relates to the technical field of digital image feature extraction, in particular to a method for rapid detection of weft skewness of fabrics based on machine vision. Background technique [0002] During the processing of knitwear and textiles, dyeing, washing, printing and other processes make the fabric subject to uneven internal stress. Weft skew will seriously affect the appearance and texture of clothing. The traditional photoelectric theodolite has the shortcomings of detection dead angle and low detection accuracy, while the CCD-based digital image feature extraction technology is more and more widely used in the textile industry, such as fabric density detection based on image processing, fabric defect detection Wait. At present, there are two solutions for machine vision detection of fabric weft bias: [0003] One solution is to smooth the fabric image by using morphological transformation, use morphological corrosion to elimi...

AI Technical Summary

Problems solved by technology

Although this method has a high degree of self-adaptation, like the first scheme, the algorithm complexity is relatively high, and the Fourier transform will consume a lot of time when there are many image pixels.

In addition, there are uncontrollable factors in the selection of samples in this method, which will eventually reduce the detection accuracy

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