In the last years, multiple quality control tasks consist in classifying some items based on their aesthetic characteristics (aesthetic quality control, AQC), where usually the aspect of the material is not measurable and is based on expert observation. Given the increasing amount of images in this domain, deep learning (DL) models can be used to extract and classify the most discriminative patterns. Frequently, when trying to evaluate the quality of a manufactured product, the categories are naturally ordered, resulting in an ordinal classification problem. However, the ordinal categories assigned by an expert can be arranged in different levels that somehow model a hierarchy of the AQC task. In this work, we propose a DL approach to improve the classification performance in problems where categories are naturally ordered and follow a hierarchical structure. The proposed approach is evaluated on a real-world dataset that defines an AQC task and compared with other state-of-the-art DL methods. The experimental results show that our hierarchical approach outperforms the state-of-the-art ones.

Deep learning based hierarchical classifier for weapon stock aesthetic quality control assessment / Vargas, V. M.; Gutierrez, P. A.; Rosati, R.; Romeo, L.; Frontoni, E.; Hervas-Martinez, C.. - In: COMPUTERS IN INDUSTRY. - ISSN 0166-3615. - 144:(2023). [10.1016/j.compind.2022.103786]

Deep learning based hierarchical classifier for weapon stock aesthetic quality control assessment

Rosati R.;
2023-01-01

Abstract

In the last years, multiple quality control tasks consist in classifying some items based on their aesthetic characteristics (aesthetic quality control, AQC), where usually the aspect of the material is not measurable and is based on expert observation. Given the increasing amount of images in this domain, deep learning (DL) models can be used to extract and classify the most discriminative patterns. Frequently, when trying to evaluate the quality of a manufactured product, the categories are naturally ordered, resulting in an ordinal classification problem. However, the ordinal categories assigned by an expert can be arranged in different levels that somehow model a hierarchy of the AQC task. In this work, we propose a DL approach to improve the classification performance in problems where categories are naturally ordered and follow a hierarchical structure. The proposed approach is evaluated on a real-world dataset that defines an AQC task and compared with other state-of-the-art DL methods. The experimental results show that our hierarchical approach outperforms the state-of-the-art ones.
2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/325891
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