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

Áreas de investigación:

• Sin categoría

Año:

2023

Tipo de publicación:

Artículo

Palabras clave:

Hierarchical classification, Ordinal classification, Deep learning, Aesthetic quality control, Convolutional neural networks

Autores:

• Vargas, Víctor Manuel
• Gutiérrez, Pedro Antonio
• Rosati, Riccardo
• Romeo, Luca
• Frontoni, Emanuele
• Hervás-Martínez, César

Journal:

Computers in Industry

Volumen:

144

Páginas:

1-13

Mes:

January

ISSN:

0166-3615

BibTex:

@article{DLHierarchicalClassifier, author = "V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and Riccardo Rosati and Luca Romeo and Emanuele Frontoni and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", 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 the 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.", awards = "JCR(2023): 8.2, Position: 11/169 (Q1) Category: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS", comments = "JCR(2023): 8.2, Position: 11/169 (Q1) Category: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS", doi = "10.1016/j.compind.2022.103786", issn = "0166-3615", journal = "Computers in Industry", keywords = "Hierarchical classification, Ordinal classification, Deep learning, Aesthetic quality control, Convolutional neural networks", month = "January", note = "JCR(2023): 8.2, Position: 11/169 (Q1) Category: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS", pages = "1-13", title = "{D}eep learning based hierarchical classifier for weapon stock aesthetic quality control assessment", url = "doi.org/10.1016/j.compind.2022.103786", volume = "144", year = "2023", }

Nota:

JCR(2023): 8.2, Position: 11/169 (Q1) Category: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

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 the 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.

Comentarios:

JCR(2023): 8.2, Position: 11/169 (Q1) Category: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS