Using edge detection and polarimetric imaging for vaccine inspection

Abstract

Automating visual inspection of vials containing freeze-dried products is a difficult problem due to the complex appearance of freeze-dried materials. To existing inspection equipment, defects and typical product appearance variation often appear very similar. We contend that these shortcomings necessitate a new approach and propose a multimodal sensor integration framework. We study polarimetric imaging combined with edge detection to improve the detection of defects such as scratches in glass vials. Stokes vectors enable the extraction and display of different polarization characteristics. Edge detection compares the effectiveness of RGB and polarimetric imaging for defect detection. A novel mathematical description of the outline strength decreases edge detection complexity. Our work shows that polarimetry, in combination with edge detection, enhances the detection of defects. We find that a combination of polarization characteristics in the hue-saturation-value (HSV) false coloring scheme outperforms all individual polarization characteristics and RGB imaging, achieving a 100% detection accuracy at an outline strength of 0.7. Image resolution impacts the effectiveness of edge detection as well. A resolution of 1500 x 1000 pixel works best for defect detection in our dataset. Defect detection is most sensitive to scratch depth, while scratch length and thickness are less important. Our findings not only apply to defect detection on glass vaccine vials, but also to the inspection of other reflective surfaces such as plastics and metals. This work can be expanded to problems that require material detection. Denise Tellbach is a graduate student in Mechanical Engineering at MIT. Her research interests are in IoT, sensing, and applying machine learning to enhance the functionality of industrial quality control. Denise received a double master’s degree in Management Science and Mechanical Engineering from RWTH Aachen University and Tsinghua University in 2018 and her undergraduate degree from RWTH Aachen University in 2016. In the past, she has developed a maturity model for the digitalization of production control, she has worked on cyber-physical systems modeling and on reliability assessment focusing on the electric grid. She joined the AutoID Lab as a graduate student under Prof. Sarma in 2019 and is a Presidential Fellow at MIT (2019). Praveer Sharan is a researcher affiliated with the Auto ID Lab at the Massachusetts Institute of Technology. His research interests are in data analysis, unmet needs in healthcare, edge detection, and machine learning to boost efficiency of healthcare. Praveer is scheduled to graduate in 2022 and is applying for computer science programs. In the past, he has conducted a review on the ability of neural networks to automatically discriminate between healthy versus unhealthy coughs based on the processed audio of the cough. He has worked on research projects and conducted research with the MIT computer science department for the last two summers.