Multimodal Optical Telemetry for Defect Detection in Vaccine Vials
Vaccines are a critical public health tool to prevent deaths and hospitalizations but can be difficult to produce and distribute in sufficient quantity. Freeze-drying makes vaccines thermally stable for easier distribution but visually opaque, which complicates inspection. We introduce a multimodal passive remote wireless telemetry approach that utilizes different sensing modalities to address complementary inspection challenges. Polarimetric imaging successfully differentiates between typical product appearance variation and types of defects achieving an accuracy of 96% using the number of detected clusters with statistical significance at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha =0.05$ </tex-math></inline-formula> (streaks versus fibers: t(df =28) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$= -14.062$ </tex-math></inline-formula> and fibers versus scratches: t(df =28) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$= -7.393$ </tex-math></inline-formula>). Infrared (IR) imaging is investigated to detect glass scratches otherwise obscured by reflections, nonplanar surfaces, or vaccine powder; 100% of defects on the vial neck and body are detectable, while 3/5 smaller defects on the vial bottom can be detected. We further establish detection limits for IR imaging, showing that glass scratches with a width as small as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$32~\mu $ </tex-math></inline-formula>m are detectable. Finally, we establish a weighted sum of pixel differences between frames to detect scratches during vial rotation. We show that this measure can be used to detect scratches at different rotation speeds, but the signal-to-noise ratio increases with rotation speed from 14:1 at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$12~{^{\circ}}$ </tex-math></inline-formula>/s, over 8:1 at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$24~{^{\circ}}$ </tex-math></inline-formula>/s–2.5:1 at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$45~{^{\circ}}$ </tex-math></inline-formula>/s.
Authors
- Rahul Bhattacharyya
- Sanjay E. Sarma (ORCID: https://orcid.org/0000-0003-2812-039X)
- Denise Tellbach (ORCID: https://orcid.org/0000-0001-7003-508X)
- Jia Yuan Hu (ORCID: https://orcid.org/0009-0003-1811-9229)
- Rohith Yelisetty
Institutions
- Massachusetts Institute of Technology (US)
Publication Details
- Journal
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/tim.2025.3556207
- Primary Topic
- Industrial Vision Systems and Defect Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00