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Using Object Detection Technology to Identify Defects in Clothing for Blind People
Blind people often encounter challenges in managing their clothing, specifically in identifying defects such as stains or holes. With the progress of the computer vision field, it is crucial to minimize these limitations as much as possible to assist blind people with selecting appropriate clothing....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181740/ https://www.ncbi.nlm.nih.gov/pubmed/37177584 http://dx.doi.org/10.3390/s23094381 |
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author | Rocha, Daniel Pinto, Leandro Machado, José Soares, Filomena Carvalho, Vítor |
author_facet | Rocha, Daniel Pinto, Leandro Machado, José Soares, Filomena Carvalho, Vítor |
author_sort | Rocha, Daniel |
collection | PubMed |
description | Blind people often encounter challenges in managing their clothing, specifically in identifying defects such as stains or holes. With the progress of the computer vision field, it is crucial to minimize these limitations as much as possible to assist blind people with selecting appropriate clothing. Therefore, the objective of this paper is to use object detection technology to categorize and detect stains on garments. The defect detection system proposed in this study relies on the You Only Look Once (YOLO) architecture, which is a single-stage object detector that is well-suited for automated inspection tasks. The authors collected a dataset of clothing with defects and used it to train and evaluate the proposed system. The methodology used for the optimization of the defect detection system was based on three main components: (i) increasing the dataset with new defects, illumination conditions, and backgrounds, (ii) introducing data augmentation, and (iii) introducing defect classification. The authors compared and evaluated three different YOLOv5 models. The results of this study demonstrate that the proposed approach is effective and suitable for different challenging defect detection conditions, showing high average precision (AP) values, and paving the way for a mobile application to be accessible for the blind community. |
format | Online Article Text |
id | pubmed-10181740 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101817402023-05-13 Using Object Detection Technology to Identify Defects in Clothing for Blind People Rocha, Daniel Pinto, Leandro Machado, José Soares, Filomena Carvalho, Vítor Sensors (Basel) Article Blind people often encounter challenges in managing their clothing, specifically in identifying defects such as stains or holes. With the progress of the computer vision field, it is crucial to minimize these limitations as much as possible to assist blind people with selecting appropriate clothing. Therefore, the objective of this paper is to use object detection technology to categorize and detect stains on garments. The defect detection system proposed in this study relies on the You Only Look Once (YOLO) architecture, which is a single-stage object detector that is well-suited for automated inspection tasks. The authors collected a dataset of clothing with defects and used it to train and evaluate the proposed system. The methodology used for the optimization of the defect detection system was based on three main components: (i) increasing the dataset with new defects, illumination conditions, and backgrounds, (ii) introducing data augmentation, and (iii) introducing defect classification. The authors compared and evaluated three different YOLOv5 models. The results of this study demonstrate that the proposed approach is effective and suitable for different challenging defect detection conditions, showing high average precision (AP) values, and paving the way for a mobile application to be accessible for the blind community. MDPI 2023-04-28 /pmc/articles/PMC10181740/ /pubmed/37177584 http://dx.doi.org/10.3390/s23094381 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Rocha, Daniel Pinto, Leandro Machado, José Soares, Filomena Carvalho, Vítor Using Object Detection Technology to Identify Defects in Clothing for Blind People |
title | Using Object Detection Technology to Identify Defects in Clothing for Blind People |
title_full | Using Object Detection Technology to Identify Defects in Clothing for Blind People |
title_fullStr | Using Object Detection Technology to Identify Defects in Clothing for Blind People |
title_full_unstemmed | Using Object Detection Technology to Identify Defects in Clothing for Blind People |
title_short | Using Object Detection Technology to Identify Defects in Clothing for Blind People |
title_sort | using object detection technology to identify defects in clothing for blind people |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181740/ https://www.ncbi.nlm.nih.gov/pubmed/37177584 http://dx.doi.org/10.3390/s23094381 |
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