A blister line's worst enemy is not slowness but a speed that lets defects flash past: a missing pill, a cracked cavity, an unsealed edge can all slip through the inspection window in milliseconds.
For oral solid dosage forms, blister packaging is the last physical barrier protecting patient safety. A solid-dose plant's blister line runs at tens of thousands of units per hour, needing to judge within a millisecond window whether each cavity is missing a pill, whether a tablet is cracked or broken, and whether the foil seal is intact.
The difficulty concentrates in two places: first, the strong glare from PVC blisters and aluminum foil makes conventional threshold vision misfire constantly; second, high speed blurs imaging and defect shapes vary widely, while defects such as breakage and poor sealing are inherently scarce on a high-yield line and hard to train on.
Real-Time Deep Learning on a High-Speed Line
DaoAI AI-AOI is deployed at the post-sealing station of the blister line, using deep-learning models to detect missing pills, breakage, and seal defects simultaneously, fundamentally escaping glare and threshold drift. With APDT few-shot learning, rare-defect cases such as poor sealing can be modeled and deployed quickly even when samples are insufficient; the whole solution is built for high-speed lines, inspecting in real time without slowing the beat.
- A single capture judges three defect classes at once: missing pill, breakage, poor seal
- Glare-resistant deep-learning model, ending conventional threshold false alarms
- APDT few-shot to handle rare seal defects with fast modeling
- Real-time inspection holds 50,000 units/hour without dropping line speed
Speed is not the enemy of inspection; glare and rare defects are, and deep learning solves both at once.
After go-live, overall detection accuracy reached 99% mAP, running stably at full 50,000 units/hour, with missing-pill and breakage escapes approaching zero, markedly improved seal-defect catch, sharply reduced false alarms, and QC upgraded from manual sampling to full in line inspection.
FAQ
What are the difficulties in high-speed real-time blister detection?
There are two main difficulties in high-speed real-time blister detection. Firstly, the strong reflection of PVC blisters and aluminum foil causes frequent false alarms in traditional threshold vision. Secondly, high-speed imaging is blurred, defect forms are variable, and there are few samples of rare defects, making training difficult.
How does DaoAI AI-AOI solve the blister detection problems?
DaoAI AI-AOI is deployed at the post-sealing station of the blister line. It uses a deep-learning model to detect defects, getting rid of the troubles of reflection and threshold drift. With APDT few-shot learning, it can quickly model and go online, and real-time detection doesn't slow down the production line.
What's the effect after DaoAI AI-AOI is launched?
After launch, the comprehensive detection accuracy reaches mAP 99%. It runs stably at a full speed of 50,000 pieces per hour. The missed detection of missing pills and breakages approaches zero, the detection of poor sealing is significantly improved, the false-alarm rate drops greatly, and full-line inspection is realized.