Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/85137
Título: | Blind people: clothing category classification and stain detection using transfer learning |
Autor(es): | Rocha, Daniel Soares, Filomena Oliveira, Eva Carvalho, Vítor |
Palavras-chave: | blind people clothing recognition stain detection transfer learning deep learning |
Data: | 2-Fev-2023 |
Editora: | Multidisciplinary Digital Publishing Institute |
Revista: | Applied Sciences |
Citação: | Rocha, D.; Soares, F.; Oliveira, E.; Carvalho, V. Blind People: Clothing Category Classification and Stain Detection Using Transfer Learning. Appl. Sci. 2023, 13, 1925. https://doi.org/10.3390/app13031925 |
Resumo(s): | The ways in which people dress, as well as the styles that they prefer for different contexts and occasions, are part of their identity. Every day, blind people face limitations in identifying and inspecting their garments, and dressing can be a difficult and stressful task. Taking advantage of the great technological advancements, it becomes of the utmost importance to minimize, as much as possible, the limitations of a blind person when choosing garments. Hence, this work aimed at categorizing and detecting the presence of stains on garments, using artificial intelligence algorithms. In our approach, transfer learning was used for category classification, where a benchmark was performed between convolutional neural networks (CNNs), with the best model achieving an F1 score of 91%. Stain detection was performed through the fine tuning of a deep learning object detector, i.e., the mask R (region-based)-CNN. This approach is also analyzed and discussed, as it allowed us to achieve better results than those available in the literature. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/85137 |
DOI: | 10.3390/app13031925 |
e-ISSN: | 2076-3417 |
Versão da editora: | https://www.mdpi.com/2076-3417/13/3/1925 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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applsci-13-01925.pdf | 3,27 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons