Defect detection in the textile industry using the YOLOv8 model
Abstract
The textile industry plays a fundamental role in the global economy, being responsible for the production of a wide variety of products. Defect detection in this context is a critical aspect as it presents a series of defects that include objects of reduced dimensions and inspection is often carried out manually. This article improves quality control processes in the textile industry by applying the YOLOv8 model to the public AITEX dataset for automated defect detection, representing a technological advance when compared to the YOLOv5 model, both developed by ultralytics. Furthermore, issues such as the architecture and variants of the YOLOv8 model, limitations, parameter optimization and training strategies, as well as model evaluation are discussed. Promising results are presented, reaching an mAP of 85.11%, indicating that YOLOv8 can effectively be used in the context of textile defect detection.
Keywords:
YOLOv8, AITEX, Object detection, Textile defects, Hyper parameters, Textile industry
References
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K. Ramakrishnan, P. G. Jayakumar, P. Saravanan, and P. Sivakumar, “A novel fabric defect detection network in textile fabrics based on dlt,” in 2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), 2023, pp. 1–8.
G. Jocher, A. Chaurasia, and J. Qiu, “Ultralytics yolov8,” 2023. [Online]. Available: [link]
F. I. D. AITEX, “Fabric image database aitex,” 2019. [Online]. Available: [link]
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779–788.
T. Liu and S. Chen, “Yolov4-dcn-based fabric defect detection algorithm,” in 2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC), 2022, pp. 710–715.
D. Ali Tianchi, “Database ali tianchi,” 2022. [Online]. Available: [link]
H. Huang, B. Wang, J. Xiao, and T. Zhu, “Improved small-object detection using yolov8: A comparative study,” Applied and Computational Engineering, vol. 41, pp. 80–88, 02 2024.
R. Seidel, H. S. Junior, and K. Komati, “Textile defect detection using yolov5 on aitex dataset,” in Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional. Porto Alegre, RS, Brasil: SBC, 2022, pp. 763–774. [Online]. Available: [link]
R. Seidel, C. Waiandt, and H. S. Junior, “Defect detection in textile manufacturing: Improving findings using yolov5 versions,” in Anais do XVIII Workshop de Visão Computacional. Porto Alegre, RS, Brasil: SBC, 2023, pp. 66–71. [Online]. Available: [link]
D. Wahyudi, I. Soesanti, and H. A. Nugroho, “Optimizing hyperparameters of yolo to improve performance of brain tumor detection in mri images,” in 2023 6th International Conference on Information and Communications Technology (ICOIACT), 2023, pp. 413–418.
J. S. Blanes, T. Albero, I. Miralles, R. Pérez-Llorens, and J. Moreno, “A public fabric database for defect detection methods and results,” Autex Research Journal, vol. 19, no. 4, pp. 363–374, 2019. [Online]. Available: DOI: 10.2478/aut-2019-0035
P. Roboflow, “Roboflow,” 2024. [Online]. Available: [link]
H. Ultralytics, “Ultralytics yolo docs,” mai 2024. [Online]. Available: [link]
Q. Zhou, J. Mei, Q. Zhang, S. Wang, and G. Chen, “Semi-supervised fabric defect detection based on image reconstruction and density estimation,” Textile Research Journal, vol. 91, no. 9-10, pp. 962–972, 2021.
A. Ç. Seçkin and M. Seçkin, “Detection of fabric defects with intertwined frame vector feature extraction,” Alexandria Engineering Journal, vol. 61, no. 4, pp. 2887–2898, 2022.
L. Zheng, X. Wang, Q. Wang, S. Wang, and X. Liu, “A fabric defect detection method based on improved yolov5,” in 2021 7th International Conference on Computer and Communications (ICCC). IEEE, 2021, pp. 620–624
Published
2024-11-06
How to Cite
WAIANDT, Cláudio Alberto; SEIBEL JÚNIOR, Hilário.
Defect detection in the textile industry using the YOLOv8 model. In: WORKSHOP ON INFORMATION SYSTEMS (WSIS), 15. , 2024, Rio Paranaíba/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 74-79.
DOI: https://doi.org/10.5753/wsis.2024.33676.
