Defect detection in textile manufacturing: Improving findings using YOLOv5 versions

  • Rodolfo Seidel IFES
  • Cláudio Alberto Waiandt IFES
  • Hilário Seibel Júnior IFES

Resumo


Currently one of the main challenges in identifying textile defects is related to the detection manual, mainly due to the complexity of irregular shapes and small objects. Finding ways to automate this detection has been studied by several authors. Thus this article expands and improves the findings and conclusions in the work, entitled ’Textile defect Detection using YOLOv5 on AITEX dataset’. This study deepened the investigation with native data augmentation, transfer learning and base rebalancing techniques, in addition to new metrics that allowed us to present results that corroborate and expand previous findings. Previous research laid the foundation for this study and provided crucial insights that guided further exploration of the issues. With the results obtained, it was concluded that the versions of YOLOv5 with the new techniques used adapted very well to the new context, enhancing their performance.

Palavras-chave: YOLOv5, AITEX, Detection of textile defects, Bounding boxes, IoU, Annotation of objects, Transfer learning

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Publicado
13/11/2023
SEIDEL, Rodolfo; WAIANDT, Cláudio Alberto; SEIBEL JÚNIOR, Hilário. Defect detection in textile manufacturing: Improving findings using YOLOv5 versions. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 18. , 2023, São Bernardo do Campo/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 66-71. DOI: https://doi.org/10.5753/wvc.2023.27534.