Enhanced Fabric Defect Detection Using Swin Transformer and EfficientNetFaster R-CNN
DOI:
https://doi.org/10.48047/nfnavj23Keywords:
Fabric defect detection, Textile quality control, Deep learning models, Swin Transformer, EfficientNet, Faster R-CNN, Binary classification, Alibaba Tianchi dataset, Machine vision, Model comparison.Abstract
In the textile industry, fabric defect detection plays a very important role in the process of ensuring quality control, minimizing waste, and satisfying customers. The current article evaluates and compares three state-of-the-art deep learning architectures: Swin Transformer, EfficientNet, and Faster R-CNN, for the task of fabric defect detection on the Alibaba Cloud Tianchi Guangdong Fabric Defect Detection dataset.
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References
Bao, J., Jing, J., and Xie, Y., "A Defect Detection System of Glass Tube Yarn Based on Machine Vision," Journal of Industrial Textiles, 2023. [2] Guder, O., Isik, S., and Anagun, Y., "Ensemble learning application for textile defect detection," International Journal of Applied Methods in Electronics and Computers, vol. 11, no. 3, pp. 145-150, 2023.
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