Intelligent detection method and system for food packaging

By employing a detection method combining spectral difference and adaptive filtering, the problem of insufficient accuracy in detecting micron-level defects on food packaging surfaces has been solved, achieving efficient and reliable defect identification and improving detection efficiency and accuracy.

CN121353087BActive Publication Date: 2026-06-02GUANGZHOU BEILE FOOD CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
GUANGZHOU BEILE FOOD CO LTD
Filing Date
2025-11-13
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing technologies struggle to efficiently detect micron-level defects on food packaging surfaces in complex and dynamic industrial environments, especially tiny defects on aluminum foil seals, beverage can bottoms, and brushed metal casings. Traditional methods are inefficient, lack precision, and are sensitive to changes in lighting.

Method used

A detection method based on spectral difference and adaptive filtering is adopted. By acquiring the original image, applying geometric perturbation, calculating the spectral difference signal, establishing a benchmark model, constructing an adaptive filter, and performing dynamic frequency domain filtering and spatial domain image reconstruction, defect identification is achieved.

Benefits of technology

It significantly improves the accuracy and sensitivity of food packaging defect detection, effectively suppresses noise interference, and enhances the reliability and automation level of detection.

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Abstract

The present application relates to the technical field of intelligent detection, in particular to a kind of intelligent detection method and system for food packaging.Method includes: obtaining the original image of the measured film;The original image is applied to the preset geometric disturbance, and auxiliary image is obtained;Original image and auxiliary image are executed two-dimensional fast Fourier transform, and frequency spectrum difference signal is calculated;The benchmark model of standard sample is established, the frequency spectrum difference signal of the measured film is compared with benchmark model to obtain frequency domain abnormality;According to frequency domain abnormality, self-adapting filter is constructed, and is applied to original image to filter;After filtering, the frequency spectrum data is executed two-dimensional inverse fast Fourier transform to identify defect.The present application significantly improves the accuracy and precision of defect detection under periodic texture background.
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