Packaging decoration quality inspection method based on machine vision
A technology of machine vision and quality assessment, applied in the field of artificial intelligence, can solve problems such as missed inspection, low efficiency of product packaging materials and printing and decoration quality inspection, wrong inspection, etc., to ensure technical effects, improve quality inspection efficiency and accuracy of quality inspection rate effect
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Embodiment 1
[0030] Please refer to the attached figure 1 , the embodiment of the present application provides a packaging and decoration quality inspection method based on machine vision, wherein the method is applied to a packaging and decoration quality inspection system based on machine vision, and the method specifically includes the following steps:
[0031] Step S100: Collect the first product decoration image by the first image sensor, input it into the first preprocessing module, and obtain the first image set, wherein the first image set is a multi-dimensional collection result;
[0032] Specifically, the machine vision-based packaging and decoration quality inspection method is applied to the machine vision-based packaging and decoration quality inspection system, which can intelligently monitor the quality and printing quality of product packaging materials in real time based on computer vision technology. Improve the quality of packaging and decoration, thereby improving the e...
Embodiment 2
[0096] Based on the same inventive concept as the machine vision-based packaging and decoration quality inspection method in the foregoing embodiments, the present invention also provides a machine vision-based packaging and decoration quality inspection system, please refer to the attached Figure 5 , the system includes:
[0097] The first obtaining unit 11: the first obtaining unit 11 is used to collect the first product decoration image through the first image sensor, input it into the first preprocessing module, and obtain the first image set, wherein the first image set is multi-dimensional collection results;
[0098] The second obtaining unit 12: the second obtaining unit 12 is configured to sequentially perform feature extraction on the first set of images to obtain a first set of image features, wherein the first set of image features includes a first subset of appearance features and the first subset of compositional features;
[0099] The third obtaining unit 13:...
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