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

摘要

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.