OCSTN: One-class time-series classification approach using a signal transformation network into a goal signal

Hayashi, Toshitaka; Cimr, Dalibor; Studnička, Filip; Fujita, Hamido; Bušovský, Damián

doi:10.1016/j.ins.2022.09.027

Cited by 9 publications

(1 citation statement)

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“…One-class classification (OCC), which identifies objects belonging to a specific class given only positive samples of that class, is attracting attention as a solution to this problem [30][31][32][33][34][35][36][37][38][39][40]. Unlike general machine-learning-based classification algorithms, the OCC model aims to learn a classification boundary that separates the target class from other classes in the input space.…”

Section: Introductionmentioning

confidence: 99%

Two-Stream Network One-Class Classification Model for Defect Inspections

Lee

Luo

Lee³

et al. 2023

Sensors

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Defect inspection is important to ensure consistent quality and efficiency in industrial manufacturing. Recently, machine vision systems integrating artificial intelligence (AI)-based inspection algorithms have exhibited promising performance in various applications, but practically, they often suffer from data imbalance. This paper proposes a defect inspection method using a one-class classification (OCC) model to deal with imbalanced datasets. A two-stream network architecture consisting of global and local feature extractor networks is presented, which can alleviate the representation collapse problem of OCC. By combining an object-oriented invariant feature vector with a training-data-oriented local feature vector, the proposed two-stream network model prevents the decision boundary from collapsing to the training dataset and obtains an appropriate decision boundary. The performance of the proposed model is demonstrated in the practical application of automotive-airbag bracket-welding defect inspection. The effects of the classification layer and two-stream network architecture on the overall inspection accuracy were clarified by using image samples collected in a controlled laboratory environment and from a production site. The results are compared with those of a previous classification model, demonstrating that the proposed model can improve the accuracy, precision, and F1 score by up to 8.19%, 10.74%, and 4.02%, respectively.

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