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Online recognition method for zip-top can smooth curved surface coding based on machine vision

A technology of machine vision and recognition methods, applied in the field of visual inspection, can solve the problems of high detection cost, false detection and missed detection, and increased difficulty of human eye detection

Inactive Publication Date: 2017-12-08
GUANGDONG UNIV OF TECH
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Problems solved by technology

At present, the coding on the surface of cans is mainly based on manual subjective detection, but due to the complexity of the background pattern of cans, it is more difficult to detect with human eyes, and it is easy to cause false detection and missed detection, and the efficiency of manual detection is low, and the detection cost is relatively high. High, not conducive to the development requirements of enterprises

Method used

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  • Online recognition method for zip-top can smooth curved surface coding based on machine vision
  • Online recognition method for zip-top can smooth curved surface coding based on machine vision
  • Online recognition method for zip-top can smooth curved surface coding based on machine vision

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Embodiment Construction

[0040] The present invention will be further described below in conjunction with specific embodiment:

[0041] See attached figure 1 As shown, a machine vision-based online recognition method for smooth curved surface coding of pop cans described in this embodiment includes the following steps:

[0042] S1. Using a linear array CCD camera in an area where the brightness can be adjusted, the spherical integral light source is used to shoot from multiple angles, and the original image of the characters sprayed on the curved surface of the can is collected.

[0043] S2. Perform grayscale and median filtering on the image:

[0044] Find the average value g(i,j) of the M*N window neighborhood of each pixel in the original image area;

[0045] The calculation formula of median filter is:

[0046]

[0047] Among them, f(i, j) represents the gray value of each point in the original image, g(i, j) represents the gray value of each point in the filtered image, and A represents the...

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Abstract

The invention relates to an online recognition method for zip-top can smooth curved surface coding based on machine vision. The online recognition method includes the steps of S1, collecting an original image of zip-top can smooth curved surface coding characters; S2, preprocessing the collected original image; S3, segmenting a coding region in the preprocessed image; S4, segmenting and normalizing the coding characters; and S5, recognizing the characters. The method of the invention can replace the artificial recognition by the machine vision so as to realize the online recognition of the zip-top can smooth curved surface coding with the high precision and high efficiency while ensuring a low detection cost, a low false detection rate and a low detection omission rate.

Description

technical field [0001] The invention relates to the technical field of visual detection, in particular to an online recognition method for coding on smooth curved surfaces of pop cans based on machine vision. Background technique [0002] Production date coding on the surface of cans is an important indicator of the quality of canned beverages. At present, the coding on the surface of cans is mainly based on manual subjective detection, but due to the complexity of the background pattern of cans, it is more difficult to detect with human eyes, and it is easy to cause false detection and missed detection, and the efficiency of manual detection is low, and the detection cost is relatively high. High, not conducive to the development requirements of enterprises. [0003] How to ensure that the detection cost and the false detection and missed detection rate are low can also realize the online recognition of the smooth surface of the cans with high precision and efficiency, whi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/34G06K9/32G06T7/11G06T7/136G06T7/194
CPCG06T7/11G06T7/136G06T7/194G06T2207/20084G06T2207/10004G06T2207/20032G06V30/158G06V10/25G06V30/153
Inventor 汪南辉尹志锋张平张美杰陈振州
Owner GUANGDONG UNIV OF TECH
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