Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/85618
Título: | Using object detection technology to identify defects in clothing for blind people |
Autor(es): | Rocha, Daniel Pinto, Leandro Machado, José Soares, Filomena Carvalho, Vítor |
Palavras-chave: | Blind people Clothing defect detection Object detection Deep learning YOLOv5 |
Data: | 28-Abr-2023 |
Editora: | Multidisciplinary Digital Publishing Institute (MDPI) |
Revista: | Sensors |
Citação: | Rocha, D.; Pinto, L.; Machado, J.; Soares, F.; Carvalho, V. Using Object Detection Technology to Identify Defects in Clothing for Blind People. Sensors 2023, 23, 4381. https://doi.org/10.3390/s23094381 |
Resumo(s): | 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. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/85618 |
DOI: | 10.3390/s23094381 |
ISSN: | 1424-8220 |
e-ISSN: | 1424-8220 |
Versão da editora: | https://www.mdpi.com/1424-8220/23/9/4381 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals MEtRICs - Artigos em revistas internacionais/Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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sensors-23-04381-v2.pdf | 3,31 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons