Textile defect detection using YOLOv5 on AITEX Dataset

  • Rodolfo Seidel IFES
  • Hilário Seibel Júnior IFES
  • Karin Satie Komati IFES

Resumo


Devido à identificação manual de defeitos têxteis ainda nos dias atuais, é necessário encontrar meios de detectar defeitos de forma automatizada e eficiente. Para isso, este trabalho se propõe a aplicar o modelo YOLOv5 na base de dados AITEX, usando a abordagem de detecção de objetos para localizar e identificar defeitos, avaliando diferentes técnicas de anotação de objetos e data augmentation. Com os resultados obtidos, concluiu-se que o YOLOv5 adaptou-se muito bem a outro contexto com objetos distintos do prétreinamento, as anotações com Bounding Boxes permitiram maior aprendizado e reconhecimento dos defeitos, mesmo com diferentes formas e tamanhos, e por fim, a combinação de data augmentation potencializam seu desempenho.

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Publicado
28/11/2022
SEIDEL, Rodolfo; SEIBEL JÚNIOR, Hilário; KOMATI, Karin Satie. Textile defect detection using YOLOv5 on AITEX Dataset. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 19. , 2022, Campinas/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 763-774. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2022.227396.

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