Jar cover surface defect detection method based on machine vision

Abstract

The invention discloses a jar cover surface defect detection method based on machine vision. The method comprises the following steps that: obtaining a jar cover image, and calculating the center of gravity coordinate (Xg, Yg) of the jar cover image; obtaining jar cover edge points, and adopting a three-point random circle detection method to carry out fitting on the jar cover edge points to obtain a fit circle, wherein the fit circle is a jar cover area; dividing the jar cover area into four areas, and independently radially unfolding four areas; obtaining the dimension features of a center area, carrying out classified identification on the basis of a preset center area classifier to obtain the defect detection result of a center area; and independently obtaining the dimension features of an embedded ring area, a glue injection area and an edge curling area, and on the basis of a preset embedded ring area classifier, a glue injection area classifier and an edge curling area classifier, carrying out classified identification to obtain the defect detection results of the embedded ring area, the glue injection area and the edge curling area. The method is quick in implementation andaccurate in positioning, an error problem caused by the irregular texture information of the center area is overcome, and the robustness of the method is better.

Description

technical field [0001] The invention belongs to the technical field of machine vision and industrial automation detection, and in particular relates to a method for detecting surface defects of can lids based on machine vision. Background technique [0002] With the advent of Industry 4.0, in the automated production line of the food and beverage industry, can lids are an important part of canned beverages, and their complex structure makes it prone to various types of defects. Defective can lids may cause food safety accidents and even endanger the lives of consumers. Therefore, before using can lids to seal cans, it is necessary to detect whether there are defects. However, the artificial defect detection method is slow in detection speed, prone to fatigue, and the detection results are easily affected by human subjective factors. Manual detection may also cause damage to the cans. cover causing contamination. The defect detection method based on machine vision has fast ...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is a machine vision-based detection method for surface defects of can lids, which can realize rapid positioning of the can lid area, ensure the positioning speed and overcome the interference of incomplete edge or large defect deformation of the can lid, and realize accurate positioning ; It also overcomes the problem of large errors when there is irregular texture information in the central area, and the robustness of the detection method is stronger

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