Smoothness evaluation method of seams of clothing fabrics

Abstract

The invention discloses a smoothness evaluation method of seams of clothing fabrics, comprising the following steps: converting a seam smoothness standard sample picture into a gray-scale image; carrying out wavelet transformation on the gray-scale image and extracting image characteristic parameters; using the image characteristic parameters as input information parameters, using the seam smoothness grades as output information parameters, and constructing and obtaining a probabilistic neural network by training of a plurality of training samples; and using the probabilistic neural network to carry out inspection on a sewing sample of the clothing fabric and obtaining an evaluation result of the seam smoothness of the sewing sample. Based on objective evaluation of appearance quality of the seams of the probabilistic neural network, the smoothness evaluation method adopts an all-way full-automatic no-intervention system, solves the problems of fuzzy evaluation result and poor reproducibility which are caused by the reason that the smoothness inspection of the seams of the clothing fabrics is easily influenced by the individual mood and experience of an evaluator.

Description

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine

Login to View More

AI Technical Summary

Benefits of technology

This innovation improves upon existing systems for inspecting fabrics by providing an automatic way to measure their surface's smoothness without requiring manual interference from operators or other technical means like measuring tools.

Problems solved by technology

Technically speaking, current objective method for measuring fabric quality involves comparing different colors or patterns in order to determine if there should be any defects that may occur during manufacture. However this process can only provide limited feedback about how well these products look when worn outdoors over time due to changes caused by factors like sunlight exposing them more frequently than usual.

Images