ELCVIA Electronic Letters on Computer Vision and Image Analysis (Jun 2023)

Infrared Thermography For Seal Defects Detection On Packaged Products: Unbalanced Machine Learning Classification With Iterative Digital Image Restoration

  • Victor Guillot

DOI
https://doi.org/10.5565/rev/elcvia.1567
Journal volume & issue
Vol. 22, no. 1

Abstract

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Non-destructive and online defect detection on seals is increasingly being deployed in packaging processes, especially for food and pharmaceutical products. It is a key control step in these processes as it curtails the costs of these defects. To address this cause, this paper highlights a combination of two cost-effective methods, namely machine learning algorithms and infrared thermography. Expectations can, however, be restricted when the training data is small, unbalanced, and subject to optical imperfections. This paper proposes a classification method that tackles these limitations. Its accuracy exceeds 93% with two small training sets, including 2.5 to 10 times fewer negatives. Its algorithm has a low computational cost compared to deep learning approaches, and does not need any prior statistical studies on defects characterization.

Keywords