One-class Classification-based Acoustic Inspection Method for Canned Foods

DOI

10.2991/masta-19.2019.70How to use a DOI?

Keywords

Canned foods inspection, Acoustic detection, One-class classification, Semi-Non-negative matrix factorization

Abstract

It is significant to inspect whether the vacuum degree of food container meets the standard. This paper proposes to treat it as a one-class classification problem. And for this, we present a one-class classification algorithm based on semi-non-negative matrix factorization: the classifier only needs to be learned from the dataset of qualified products, and then it can be used to judge whether the vacuum of the detected product is qualified or not. The detection results show that the proposed method can not only acquire the highest detection accuracy, but also correctly distinguishes the unqualified types that are easy to be misjudged by traditional methods.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Wei Han
AU  - Song-bin Zhou
AU  - Chang Li
AU  - Yi-sen Liu
AU  - Wei-xin Liu
PY  - 2019/07
DA  - 2019/07
TI  - One-class Classification-based Acoustic Inspection Method for Canned Foods
BT  - Proceedings of the 2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019)
PB  - Atlantis Press
SP  - 414
EP  - 419
SN  - 1951-6851
UR  - https://doi.org/10.2991/masta-19.2019.70
DO  - 10.2991/masta-19.2019.70
ID  - Han2019/07
ER  -