A novel deep ordinal classification approach for aesthetic quality control classification

Research areas:

• Uncategorized

Year:

2022

Type of Publication:

Article

Keywords:

Ordinal classification, Deep ordinal models, Quality control, Decision support system

Authors:

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

Journal:

Neural Computing and Applications

Volume:

34

Pages:

11625-11639

Month:

March

ISSN:

0941-0643

BibTex:

@article{DeepOrdinalClassificationAAQ2022, author = "Riccardo Rosati and Luca Romeo and V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Emanuele Frontoni", abstract = "Nowadays, decision support systems (DSSs) are widely used in several application domains, from industrial to healthcare and medicine fields. Concerning the industrial scenario, we propose a DSS oriented to the aesthetic quality control (AQC) task, which has quickly established itself as one of the most crucial challenges of Industry 4.0. Taking into account the increasing amount of data in this domain, the application of machine learning (ML) and deep learning (DL) techniques offers great opportunities to automatize the overall AQC process. State-of-the-art is mainly oriented to approach this problem with a nominal DL classification method which does not exploit the ordinal structure of the AQC task, thus not penalizing the error among distant AQC classes (which is a relevant aspect for the real use case). The paper introduces a DL ordinal methodology for the AQC classification. Differently from other deep ordinal methods, we combined the standard categorical cross-entropy with the cumulative link model and we imposed the ordinal constraint via the thresholds and slope parameters. Experimental results were performed for solving an AQC task on a novel image dataset originated from a specific company{\&}#039;s demand (i.e., aesthetic assessment of wooden stocks). We demonstrated how the proposed methodology is able to reduce misclassification errors (up to 0.937 quadratic weight kappa loss) among distant classes while overcoming other state-of-the-art deep ordinal models and reducing the bias factor related to the item geometry. The proposed DL approach was integrated as the main core of a DSS supported by Internet of Things (IoT) architecture that can support the human operator by reducing up to 90% the time needed for the qualitative analysis carried out manually in this specific domain.", awards = "JCR (2022): 6.0 Position: 41/145 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR (2022): 6.0 Position: 41/145 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1007/s00521-022-07050-6", issn = "0941-0643", journal = "Neural Computing and Applications", keywords = "Ordinal classification, Deep ordinal models, Quality control, Decision support system", month = "March", note = "JCR (2022): 6.0 Position: 41/145 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "11625--11639", title = "{A} novel deep ordinal classification approach for aesthetic quality control classification", url = "dx.doi.org/10.1007/s00521-022-07050-6", volume = "34", year = "2022", }

Note:

JCR (2022): 6.0 Position: 41/145 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Abstract:

Nowadays, decision support systems (DSSs) are widely used in several application domains, from industrial to healthcare and medicine fields. Concerning the industrial scenario, we propose a DSS oriented to the aesthetic quality control (AQC) task, which has quickly established itself as one of the most crucial challenges of Industry 4.0. Taking into account the increasing amount of data in this domain, the application of machine learning (ML) and deep learning (DL) techniques offers great opportunities to automatize the overall AQC process. State-of-the-art is mainly oriented to approach this problem with a nominal DL classification method which does not exploit the ordinal structure of the AQC task, thus not penalizing the error among distant AQC classes (which is a relevant aspect for the real use case). The paper introduces a DL ordinal methodology for the AQC classification. Differently from other deep ordinal methods, we combined the standard categorical cross-entropy with the cumulative link model and we imposed the ordinal constraint via the thresholds and slope parameters. Experimental results were performed for solving an AQC task on a novel image dataset originated from a specific company's demand (i.e., aesthetic assessment of wooden stocks). We demonstrated how the proposed methodology is able to reduce misclassification errors (up to 0.937 quadratic weight kappa loss) among distant classes while overcoming other state-of-the-art deep ordinal models and reducing the bias factor related to the item geometry. The proposed DL approach was integrated as the main core of a DSS supported by Internet of Things (IoT) architecture that can support the human operator by reducing up to 90% the time needed for the qualitative analysis carried out manually in this specific domain.

Comments:

JCR (2022): 6.0 Position: 41/145 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE