System for detecting quality of metal cap based on machine vision

Abstract

The invention relates to a system and method for detecting the quality of a metal cap based on machine vision, used for detecting the quality defect of the metal cap and rejecting the inferior quality product. The detection system comprises an optical imaging device, an image processing device, a rejecting device and a conveying belt, wherein the optical imaging device comprises a planar array industrial camera, a first optical detection sensor and a light source; the image processing device is used for processing the image; and the rejecting device comprises a control circuit board, a second optical detection sensor, a solenoid valve and an injection tube. The method for detecting the quality of the metal cap based on machine vision comprises the following steps of: acquiring an interested maximum outline scale of a template cap image; establishing a rotational invariance characteristic template matrix for the template cap image; acquiring a round outline of the cap to be detected according to the interested maximum outline scale; calculating the rotational invariance characteristic matrix in the coverage of the round outline of the cap to be detected; and matching the rotational invariance characteristic matrix with the rotational invariance characteristic template matrix, so as to judge whether the cap to be detected is a qualified product or an inferior product.

Description

technical field [0001] The invention belongs to the field of image sensing technology and computer control technology, and in particular relates to an online quality detection system and method based on machine vision for detecting quality information of metal bottle caps. Background technique [0002] In the production, processing and packaging industries, it is often necessary to conduct online quality inspections on various components, and metal bottle caps are an accessory that often needs to be inspected. The quality defects of bottle caps will affect the quality of subsequent beverage or pharmaceutical production and cause losses to production enterprises. The quality inspection of bottle caps in the traditional production line is done manually. This kind of manual inspection has vague standards, is greatly affected by human subjective factors, and has low detection accuracy. Due to the high reflectivity of metal materials to light, human eye detection will cause visu...

AI Technical Summary

Problems solved by technology

These two methods have a strong dependence on the brightness of the image. If there is uneven illumination due to the specular reflection of the metal bottle cap, the effectiveness of the algorithm will be affected, and the computational complexity of the Hough transform and the least square method is high.

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