Two-Stage Preprocessing Approach for Background Normalization and Defect Segmentation on Denim Fabric Image Analysis

• Thamiris Gire Zine Neves SENAI-SP
• Diego Minatel SENAI-SP
• Alexandre da Silva Saito SENAI-SP
• Matheus de Freitas Oliveira Baffa SENAI-SP

DOI: https://doi.org/10.5753/sibgrapi.est.2024.31669

Resumo

The textile industry is one of the oldest and largest industries in the world. Detecting defects early in manufacturing enables cost reduction through fast intervention and fabric correction. Vision-based software can automatically identify defects in fabrics during preor post-production. However, this detection is challenging due to denim fabric color and texture variations, making some defects less visible than others, such as double thread and cut weft. A robust preprocessing approach can reveal intrinsic features of the data. Therefore, this paper investigates background normalization and defect segmentation to highlight features and allow better differentiation during image analysis. We propose a two-stage pipeline using a combination of filters to reduce background information and merge it with the output of a defect segmentation process based on thresholding and morphological operations. To test this hypothesis, we extracted 100 features from the processed and unprocessed images and benchmarked them using several machine learning algorithms, including a deep learning model. With the processed data, we achieved up to a 7% increase in accuracy for the evaluated metrics compared to the unprocessed experiments. This study demonstrates the feasibility of detecting defective regions in denim fabric using computer vision techniques.

Referências

JCR-VIS Credit Rating Company Limited, “Denim industry: Sector update,” [link], 2018, accessed: 2024-05-15.

Delta Máquinas Têxteis, “Textile industry in brazil: discover the panorama,” [link], 2023, accessed: 2024-05-15.

Brazilian Textile and Fashion Industry, “Brazilian textile and apparel sector for 2022,” [link], 2023, accessed: 2024-05-15.

C. Li, J. Li, Y. Li, L. He, X. Fu, and J. Chen, “Fabric defect detection in textile manufacturing: A survey of the state of the art,” Security and Communication Networks, vol. 2021, p. 1–13, May 2021. [Online]. Available: DOI: 10.1155/2021/9948808

K. Hanbay, M. F. Talu, and O. F. Özgüven, “Fabric defect detection systems and methods—a systematic literature review,” Optik, vol. 127, no. 24, p. 11960–11973, Dec. 2016. [Online]. Available: DOI: 10.1016/j.ijleo.2016.09.110

H. Balakrishnan, S. Venkataraman, and S. Jayaraman, “Fdics: A vision-based system for the identification and classification of fabric defects,” Journal of the Textile Institute, vol. 89, no. 2, p. 365–380, Jan. 1998. [Online]. Available: DOI: 10.1080/00405009808658623

H. Çelik, L. Dülger, and M. Topalbekiroğlu, “Development of a machine vision system: real-time fabric defect detection and classification with neural networks,” The Journal of The Textile Institute, vol. 105, no. 6, p. 575–585, Sep. 2013. [Online]. Available: DOI: 10.1080/00405000.2013.827393

A. Song, Y. Han, H. Hu, and J. Li, “A novel texture sensor for fabric texture measurement and classification,” IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 7, p. 1739–1747, Jul. 2014. [Online]. Available: DOI: 10.1109/TIM.2013.2293812

M. F. TALU, K. HANBAY, and M. HATAMİ VARJOVİ, “Cnn-based fabric defect detection system on loom fabric inspection,” Tekstil ve Konfeksiyon, vol. 32, no. 3, p. 208–219, Sep. 2022. [Online]. Available: DOI: 10.32710/tekstilvekonfeksiyon.1032529

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, Dec. 2021. [Online]. Available: DOI: 10.1109/ICCC54389.2021.9674548

A. Durmusoglu and Y. Kahraman, “Detection of fabric defects using convolutional networks,” in 2021 Innovations in Intelligent Systems and Applications Conference (ASYU). IEEE, Oct. 2021. [Online]. Available: DOI: 10.1109/ASYU52992.2021.9599071

J. Barman, H.-C. Wu, and C.-F. J. Kuo, “Development of a real-time home textile fabric defect inspection machine system for the textile industry,” Textile Research Journal, vol. 92, no. 23–24, p. 4778–4788, Jul. 2022. [Online]. Available: DOI: 10.1177/00405175221111477

R. Thakur, D. Panghal, P. Jana, Rajan, and A. Prasad, “Automated fabric inspection through convolutional neural network: an approach,” Neural Computing and Applications, vol. 35, no. 5, p. 3805–3823, Oct. 2022. [Online]. Available: DOI: 10.1007/s00521-022-07891-1

J. J. M. Griethuysen, A. Fedorov, C. Parmar, A. Hosny, N. Aucoin, V. Narayan, R. G. Beets-Tan, J.-C. Fillon-Robin, S. Pieper, and H. J. Aerts, “Computational radiomics system to decode the radiographic phenotype,” Cancer Research, vol. 77, no. 21, pp. e104–e107, 2017. [Online]. Available: DOI: 10.1158/0008-5472.CAN-17-0339

L. P. Coelho, “Mahotas: Open source software for scriptable computer vision,” Journal of Open Research Software, vol. 1, no. 1, p. e3, 2013. [Online]. Available: DOI: 10.5334/jors.ac