OBJECTIVE To research on the application of machine monitoring under artificial intelligence visual recognition for key stages of the vaccine production process is conducted.
METHODS Selected two scenarios: the receipt of the virus strain and the addition of the inactivating agent, and designed corresponding algorithmic processes. Deep learning models were utilized for training.
RESULTS Machine vision technology could effectively monitor key steps in the vaccine production process. Through extensive data training and optimization, the average accuracy of the algorithms achieved over 90%, effectively reducing human errors or deviations while enhancing production efficiency and product quality levels.
CONCLUSION This research provides a reference for the application of machine vision technology in the vaccine production process and supports the intelligent development of the vaccine industry.
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