Deep learning based hierarchical classifier for weapon stock aesthetic quality control assessment
Víctor Manuel Vargas-Yun 
Pedro Antonio Gutiérrez 
Riccardo Rosati 
Luca Romeo 
César Hervás-Martínez Computers in Industry
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.
Keywords
BibTex Citation
@article{Vargas2023Deep,
author = {Vargas-Yun, V{\' i}ctor Manuel and Guti{\' e}rrez, Pedro Antonio and Rosati, Riccardo and Romeo, Luca and Frontoni, Emanuele and Herv{\' a}s-Mart{\' i}nez, C{\' e}sar},
journal = {Computers in Industry},
doi = {10.1016/j.compind.2022.103786},
year = {2023},
pages = {1--13},
title = {Deep learning based hierarchical classifier for weapon stock aesthetic quality control assessment},
url = {https://doi.org/10.1016/j.compind.2022.103786},
howpublished = {https://doi.org/10.1016/j.compind.2022.103786},
volume = {144},
}
BibTex Unicode Citation
@article{Vargas2023Deep,
author = {Vargas-Yun, Víctor Manuel and Gutiérrez, Pedro Antonio and Rosati, Riccardo and Romeo, Luca and Frontoni, Emanuele and Hervás-Martínez, César},
journal = {Computers in Industry},
doi = {10.1016/j.compind.2022.103786},
year = {2023},
pages = {1--13},
title = {Deep learning based hierarchical classifier for weapon stock aesthetic quality control assessment},
url = {https://doi.org/10.1016/j.compind.2022.103786},
howpublished = {https://doi.org/10.1016/j.compind.2022.103786},
volume = {144},
}
APA Citation
Vargas-Yun, V. M., Gutiérrez, P. A., Rosati, R., Romeo, L., Frontoni, E., & Hervás-Martínez, C. (2023). Deep learning based hierarchical classifier for weapon stock aesthetic quality control assessment. Computers in Industry, 144, 1–13. https://doi.org/10.1016/j.compind.2022.103786
RIS Citation
TY - JOUR
AU - Vargas-Yun, Víctor Manuel
AU - Gutiérrez, Pedro Antonio
AU - Rosati, Riccardo
AU - Romeo, Luca
AU - Frontoni, Emanuele
AU - Hervás-Martínez, César
DA - 2023///
PY - 2023
DO - 10.1016/j.compind.2022.103786
ID - temp_id_136147204383
SP - 1-13
T2 - Computers in Industry
TI - Deep learning based hierarchical classifier for weapon stock aesthetic
quality control assessment
UR - https://doi.org/10.1016/j.compind.2022.103786
VL - 144
ER -
CV Citation
V.M. Vargas-Yun (CA), P.A. Gutiérrez, R. Rosati, L. Romeo, E. Frontoni, C. Hervás-Martínez (1/6). "Deep learning based hierarchical classifier for weapon stock aesthetic quality control assessment". Computers in Industry, Vol. 144, pp. 1-13, 2023. (Q1D1, IF: 8.2).