Textile defect detection using YOLOv5 on AITEX Dataset

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

DOI: https://doi.org/10.5753/eniac.2022.227396

Abstract

Due to the manual identification of textile defects still nowadays, it is necessary to find methods to detect defects in an automated and efficient way. Hence, this work applies YOLOv5 model in the AITEX dataset, using an object detection approach to locate and identify defects, evaluating different object annotation and image data augmentation techniques. From the results, it is concluded that YOLOv5 handled very well another context with distinct objects from the pre-training, annotations using Bounding Boxes allowed greater learning and recognition of defects, even with different shapes and sizes, and at last, the combination of augmentations boosted its performance.

References

Arlen, T. C. (2018). Understanding the map evaluation metric for object detection. [link].

Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y. M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.

Conceição, A. M. G. (1998). Critérios de classificação de tecidos quanto à qualidade. [link].

Gevorgyan, Z. (2022). Siou loss: More powerful learning for bounding box regression. arXiv preprint arXiv:2205.12740.

Jin, R. and Niu, Q. (2021). Automatic fabric defect detection based on an improved yolov5. Mathematical Problems in Engineering, 2021.

Jing, J., Wang, Z., Rätsch, M., and Zhang, H. (2022). Mobile-unet: An efficient convolutional neural network for fabric defect detection. Textile Research Journal, 92(12):30-42.

Jing, J., Zhuo, D., Zhang, H., Liang, Y., and Zheng, M. (2020). Fabric defect detection using the improved yolov3 model. Journal of engineered fibers and fabrics, 15:1558925020908268.

Jocher, G., Nishimura, K., Mineeva, T., and Vilariño, R. (2020). Yolov5 (2020). GitHub repository: https://github.com/ultralytics/yolov5.

Jun, X., Wang, J., Zhou, J., Meng, S., Pan, R., and Gao, W. (2021). Fabric defect detection based on a deep convolutional neural network using a two-stage strategy. Textile Research Journal, 91(1-2):130-142.

Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L. (2014). Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer.

Ouyang, W., Xu, B., Hou, J., and Yuan, X. (2019). Fabric defect detection using activation layer embedded convolutional neural network. IEEE Access, 7:70130-70140.

Petronas, I. I. (2020). Indústria têxtil 4.0: quais são as novidades para este setor? https://inovacaoindustrial.com.br/industria-textil-40/.

Popkova, E. G., Ragulina, Y. V., and Bogoviz, A. V. (2019). Fundamental differences of transition to industry 4.0 from previous industrial revolutions. In Industry 4.0: Industrial Revolution of the 21st Century, pages 21-29. Springer.

Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2015). You look only once: unified real-time object detection. arXiv preprint arXiv:1506.02640.

Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019). Generalized intersection over union: A metric and a loss for bounding box regression. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 658-666.

Rong-qiang, L., Ming-hui, L., Jia-chen, S., and Yi-bin, L. (2021). Fabric defect detection method based on improved u-net. In Journal of Physics: Conference Series, volume 1948, page 012160. IOP Publishing.

Sakkos, D., Shum, H. P., and Ho, E. S. (2019). Illumination-based data augmentation for robust background subtraction. In 2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), pages 1-8. IEEE.

Seçkin, A. Ç. and Seçkin, M. (2022). Detection of fabric defects with intertwined frame vector feature extraction. Alexandria Engineering Journal, 61(4):2887-2898.

Silvestre-Blanes, J., Albero Albero, T., Miralles, I., Pérez-Llorens, R., and Moreno, J. (2019). A public fabric database for defect detection methods and results. Autex Research Journal, 19(4):363-374.

Wang, Y., Hao, Z., Zuo, F., and Pan, S. (2021). A fabric defect detection system based improved yolov5 detector. In Journal of Physics: Conference Series, volume 2010, page 012191. IOP Publishing.

Wieler, M., Hahn, T., and Hamprecht, F. A. (2007). Weakly supervised learning for industrial optical inspection. [dataset]. [link].

Zheng, L., Wang, X., Wang, Q., Wang, S., and Liu, X. (2021). A fabric defect detection method based on improved yolov5. In 2021 7th International Conference on Computer and Communications (ICCC), pages 620-624. IEEE.

Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020). Distance-iou loss: Faster and better learning for bounding box regression. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 12993-13000.

Zhou, Q., Mei, J., Zhang, Q., Wang, S., and Chen, G. (2021). Semi-supervised fabric defect detection based on image reconstruction and density estimation. Textile Research Journal, 91(9-10):962-972.