Pharmaceutical/Medical

Vaccine Barcode Reading

Ensure the accuracy of all required serialization and tracking codes

Barcode reader reading Data Matrix codes on ampoules

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Deep Learning

深度學習

Cognex ViDi 是基於深度學習的圖像分析軟體,專為製造業而設計。

Fixed-mount barcode readers

固定式讀碼器

具備視覺的讀碼器不僅簡單易用,而且成本等同於雷射掃描器

Regulations require that every individual vial, ampoule, or syringe (primary packaging) has a unique serial and lot number and matching GS-1 compliant barcode. These codes are read after the vial and syringe filling process. Secondary packaging has a corresponding serialized barcode that must be matched to the codes on the primary packaging. Prefilled syringes, considered combination medical devices, must also have a unique device identification (UDI) code. These can be 1D or 2D codes depending on available space and use.

These codes authenticate and track each individual unit from production to final use. Typically, when a vaccine is first introduced supplies are limited and demand is high, as is the risk of counterfeit supplies on the market. These factors make serialization and authentication essential.

Additional process and manufacturer-specific barcodes are often also present on the secondary vaccine packaging. These codes include expiration dates and other information in the form of printed text for internal tracking.

Individual or serialized barcodes need to be read quickly and accurately to confirm and track product code, batch identification, expiration date, and serial number information. This is best done with a dedicated image-based barcode reader. Because of the shortage of real estate on vials and syringes, barcodes are often very small and printed or etched on surfaces that can only be observed at extreme angles.

Cognex DataMan fixed-mount barcode readers decipher 1D and 2D codes as small as 3 mil on medical devices. The integrated lighting and High Dynamic Range Plus (HDR+) technology provides advanced image formation to reliably read low contrast codes or codes at extreme angles. These codes are captured and stored for later analysis of no-reads and mis-reads for process improvement.

While human-readable codes can be read with conventional vision systems using optical character recognition (OCR) technology, Cognex Deep Learning is able to read deformed, skewed, or poorly etched codes on the primary package. Thanks to the pretrained font library and the easy-to-use interface, Cognex Deep Learning eliminates complex, time-consuming programming. Simply define the region of interest, set the character size, and label the characters in the image set. In just a few steps, without vision expertise, this robust tool is retrained to read application-specific characters that traditional OCR tools are not able to decode, improving end-to-end traceability and helping combat counterfeits.

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