Currently, the Omicron strain of COVID-19 is spreading rapidly, and according to experts, existing vaccines have limited effectiveness against it. The development of COVID-19 vaccines remains a focus for countries, and companies are facing immense pressure in the production and distribution of vaccines in large numbers of vials, ampoules, and pre-filled syringes. To keep up with growing demand, manufacturers are turning to machine vision systems to automate vaccine packaging, inspection, tracking, and distribution.
Solutions for vaccine applications:
Machine vision and deep learning systems can help detect defects such as scratches and punctures on vial caps, improving product quality and reducing waste.
Deep learning systems can help detect defects such as scratches, bubbles, and debris on vials and ampoules, ensuring sterility.
Even with challenges such as variability, transparency, and complex assembly shapes, deep learning systems of machine vision measurement can identify cracks, debris, and other features on the flanges of syringes.
Machine vision systems can help detect and measure the location, straightness, and other features of needle safety devices to ensure correct assembly of syringes.
Deep learning systems can help detect if ribbons on the stoppers are torn, if there is liquid between the ribbons, or if there are cracks produced during the insertion process of the stoppers into the syringe barrels.
Deep learning systems can help detect various subtle defects in injection needles that have been processed with oblique grinding, to protect patient safety and ensure correct vaccine injection. Because the medical industry requires extremely high accuracy of machine vision inspection, it is crucial to choose a professional and trustworthy camera lens manufacturer.
Deep learning systems can detect the presence of bubbles, cracks, insufficient adhesive dosage, cone problems, or other impurities in the assembly of needles and syringes.
Machine vision systems can help inspect the length of the syringe barrel, the length of the plunger, the thickness of the flange, the inner diameter, and the outer diameter of the syringe to ensure compliance with specifications.
Deep learning systems can detect printing on the curved and reflective surfaces of syringe barrels, identifying any locations where the ink is too thick, too thin, or dirty.
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