Textile defect detection method

Abstract

The invention relates to a textile defect detection method. The method comprise the steps that firstly, a to-be-detected textile image with the to-be-cut edge characteristics is collected, and a series of image preprocessing operations are performed on the collected to-be-detected image; secondly, sawteeth of the image are eliminated, and textile edges are roughly determined through an LSD straight line detection algorithm; thirdly, the accurate edge positions are obtained by transversely cutting a longitudinal average gray value variation diagram, real-time edge reference data are determined according to relevant parameters of an acquisition system, and then extracted edge information and the edge reference data are compared to determine the edges and count; defect distribution is determined by combining standard diagram characteristic parameters and ignoring the defects around the edges, all defect communicating regions are obtained by adopting a region growing method, and then the number of a textile where the detects are located is judged by combining the edge positions. According to the textile defect detection method, information reference determination is repeatedly performed on the textile edges, and therefore the precision of on-line edge counting is improved; fine and scattered defects are regrown and communicated, and therefore the precision of on-line defect detection is improved.

Description

technical field [0001] The invention belongs to the field of industrial automation and relates to a defect detection method for products or workpieces. Background technique [0002] With the development of computer technology and fieldbus technology, machine vision technology is becoming more and more mature, and it has become an indispensable product for more and more enterprises in the field of design automation. In some traditional enterprises, the defects of workpieces or products still rely on sensory inspection. The introduction of machine vision instead of traditional methods can greatly improve the detection efficiency and degree of automation, especially in some high-risk working environments. Artificial vision is difficult to meet the needs of industrial automation enterprises. High efficiency and high precision requirements. In terms of some assembly line online inspection, a set of machine vision automatic inspection system with suitable design, complete applica...

AI Technical Summary

Problems solved by technology

In some traditional enterprises, the defects of workpieces or products still rely on sensory inspection. The introduction of machine vision instead of traditional methods can greatly improve the detection efficiency and automation, especially in some high-risk working environments. Artificial vision has been difficult to meet the requirements of industrial automation enterprises. Demand for high efficiency and high precision

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