| Fecha: |
2023 |
| Resumen: |
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. |
| Nota: |
Acknowledgment. This paper and the research involved in it would not have been possible without the initial support of Fredéric Roumanet. The author is also grateful to the company Thimmonier that has provided financial support to build the dataset and conduct initial studies on the subject. |
| Derechos: |
Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.  |
| Lengua: |
Anglès |
| Documento: |
Article ; recerca ; Versió publicada |
| Materia: |
Control ;
Seal ;
Machine learning ;
Small and unbalanced training set ;
Thermography ;
Iterative image restoration |
| Publicado en: |
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 22 Núm. 1 (2023) , p. 35-51 (Regular Issue) , ISSN 1577-5097 |
visitante ::
identificación
(Thimonnier)
