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Sewing defect detection using YOLOv8: a deep learning approach

Karthika Latha, Suriya Sundaramoorthy, Viju Subramoniapillai

Research Journal of Textile and Apparel (2026) 30 (3): 609–623.

https://doi.org/10.1108/RJTA-06-2025-0120

Published: 13 October 2025

...Karthika Latha; Suriya Sundaramoorthy; Viju Subramoniapillai Purpose The primary objective of this study was to evaluate the performance of the YOLOv8 ((You Only Look Once, version 8)) model in detecting several classes of sewing defects, such as skipped stitches, overlapped stitches, stains...

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Artificial neural network and regression models for prediction of sewing thread consumption for multilayered fabric assembly at lockstitch 301 seam

Md Vaseem Chavhan, M. Ramesh Naidu, Hayavadana Jamakhandi

Research Journal of Textile and Apparel (2022) 26 (4): 343–358.

https://doi.org/10.1108/RJTA-09-2020-0103

Published: 17 August 2021

...Md Vaseem Chavhan; M. Ramesh Naidu; Hayavadana Jamakhandi Purpose This paper aims to propose the artificial neural network (ANN) and regression models for the estimation of the thread consumption at multilayered seam assembly stitched with lock stitch 301. Design/methodology/approach...

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Development of prediction model through linear multiple regression for the prediction and analysis of the GSM of embroidered fabric

Anirban Dutta, Biswapati Chatterjee

Research Journal of Textile and Apparel (2020) 24 (1): 53–71.

https://doi.org/10.1108/RJTA-07-2019-0033

Published: 11 January 2020

... fabrics and study the influence of basic input parameters. Design/methodology/approach Embroidery samples are prepared taking input parameters as GSM of the base fabric, linear density of the embroidery thread and stitch density of the embroidery design. Three levels of values are identified for each...

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Sewing defect detection using YOLOv8: a deep learning approach

Artificial neural network and regression models for prediction of sewing thread consumption for multilayered fabric assembly at lockstitch 301 seam

Development of prediction model through linear multiple regression for the prediction and analysis of the GSM of embroidered fabric

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Sewing defect detection using YOLOv8: a deep learning approach

Artificial neural network and regression models for prediction of sewing thread consumption for multilayered fabric assembly at lockstitch 301 seam

Development of prediction model through linear multiple regression for the prediction and analysis of the GSM of embroidered fabric

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