Defect Detection of Can Cap Printing Based on Machine Vision

Yaqi Li; Wei Hu

doi:10.54097/w1gmvp64

Authors

Yaqi Li
Wei Hu

DOI:

https://doi.org/10.54097/w1gmvp64

Keywords:

Metal Can Lid, Printing Inspection, Template Matching, Difference Model

Abstract

To address the problems of high cost, low efficiency, and high rates of missed and incorrect detections in the production of metal can lid printing products, an online inspection system for metal can lids was designed based on machine vision. Firstly, threshold segmentation was performed using the maximum inter-class variance method, and the detection area of the can lid was extracted through feature extraction and intersection operations. For surface printing detection, a printing defect detection method based on constructing a difference model was proposed. The Sobel operator was used to extract the edge information of the image to create a difference model, and template matching and the detection area were used to process the tested image using similarity measurement, affine transformation, and scaling of gray values, etc. Finally, the obtained images were input into the difference model for the final defect detection. Experiments were conducted by detecting a large number of tested images with different defects to verify the effectiveness of the scheme.

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