Application of polarimetric imaging for automated visual inspection of freeze-dried vaccines

Rising vaccine production and complex visual characteristics of freeze-dried products have highlighted a critical need for accurate, high-speed automated quality control. Current inspection procedures, that rely on human vision or line cameras, have undesirable error rates. We propose a novel use of polarimetric imaging for defect capture and compare the performance of polarimetric imaging to RGB imaging for defect detection on vaccine vials with freeze-dried product. Vaccine vials with artificial defects (scratches and fibers) and without defects but with product appearance variations (streaks) are prepared. We capture a data set of RGB images and polarimetric images: Polarization Intensity (PI), Degree of Linear Polarization (DoLP), Angle of Polarization (AoP). We find that the differences between product variation and defects in RGB images are not statistically significant with α = 0.01 (t(8) = 2.088 for scratch vs. streak, t(8) = 2.789 for fiber vs. streak). In contrast, the differences between product variation and defects for polarimetric imaging are statistically significant for all polarization characteristics with α = 0.01 (PI: t(8) = 39.753 for scratch vs. streak, t(8) = 13.039 fiber vs. streak, DoLP: t(8) = 16.537 for scratch vs. streak, t(8) = 17.018 for fiber and streak, AoP: t(8) = 6.764 for scratch vs. streak, t(8) = 4.702 for fiber vs. streak). This indicates that polarimetric imaging may be used as a more effective technique than RGB imaging for defect detection.