One-class Classification-based Acoustic Inspection Method for Canned Foods
- https://doi.org/10.2991/masta-19.2019.70How to use a DOI?
- Canned foods inspection, Acoustic detection, One-class classification, Semi-Non-negative matrix factorization
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/masta-19.2019.70 ID - Han2019/07 ER -