FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-120107

Research Article |

Deep-GD: Deep Learning based Automatic Garment Defect Detection and Type Classification

Author(s): Dennise Mathew* and N.C Brintha

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 12, Issue 1

Publisher : FOREX Publication

Published : 05 February 2024

e-ISSN : 2347-470X

Page(s) : 41-47

DOI: https://doi.org/10.37391/IJEER.120107

Abstract

Garment defect detection has been successfully implemented in the quality quick response system for textile manufacturing automation. Defects in the production of textiles waste a lot of resources and reduce the quality of the finished goods. It is challenging to detect garment defects automatically because of the complexity of images and variety of patterns in textiles. This study presented a novel deep learning-based Garment defect detection framework named as Deep-GD model for sequentially identifying image defects in patterned garments and classify the defect types. Initially, the images are gathered from the HKBU database and bilateral filters are used in the pre-processing of images to remove distortions. A squeeze-and-excitation network (SE-net) combined with Random Decision Forest Classifier with Bayesian Optimization (RDF-BO) algorithm is used to detect and classify garment defects. By analyzing the differences among the original and reconstruction images, the unsupervised technique trains to rebuild the fabric pattern. The SE-net is used to identify certain fabric flaws in the garments of the pre-processed images. Then, the defects-related garments are processed using RDF-BO algorithm for classifying the garment defect from these regions. Finally, the proposed Deep-GD model is used for classifying defected fabric into 12 classes such as Defect free, soil stain, oil stain, Double end, Snarls, Miss, Horizontal stripes, Lumpy, Dye spot, Fall out, Hairiness, tiny hole. The proposed Deep-GD model achieves the overall classification accuracy of 97.16%, which is comparatively superior to the existing techniques.

Keywords: Garment defect, Defect classification, Deep learning, Bayesian Optimization, Random Decision Forest.




Dennise Mathew*, Research Scholar, Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Srivilliputhur, India; Email: dennisemathew@gmail.com

N.C Brintha, Associate Professor, Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Srivilliputhur, India; Email: n.c.brintha@klu.ac.in

    [1] Wang, W., Deng, N., and Xin, B. (2020). Sequential detection of image defects for patterned fabrics. IEEE Access, 8, 174751-174762. [CrossRef]
    [2] Mo, D., Wong, W.K., Lai, Z., and Zhou, J. (2020). Weighted double-low-rank decomposition with application to fabric defect detection. IEEE Transactions on Automation Science and Engineering, 18(3), 1170-1190. [CrossRef]
    [3] Loonkar, S., and Mishra, D.S. (2019). Analysis of defect classification approaches for fabric images based on four different sector features. In 2019 International Conference on Advances in Computing, Communication and Control (ICAC3), IEEE, 1-5. [CrossRef]
    [4] Xie, H., Zhang, Y., and Wu, Z. (2019). Fabric defect detection method combing image pyramid and direction template. IEEE Access, 7, 182320-182334. [CrossRef]
    [5] Wu, J., Le, J., Xiao, Z., Zhang, F., Geng, L., Liu, Y., and Wang, W. (2021) Automatic fabric defect detection using a wide-and-light network. Applied Intelligence, 51(7), pp.4945-4961. [CrossRef]
    [6] Ren, Mingzhen, Yajie Liu, and Xinmin Ma. (2023). Research on the application status of image recognition technology in textile and clothing field. In SHS Web of Conferences, EDP Sciences, 155, 03021. [CrossRef]
    [7] Ashiba, Huda I., and Mabrouka I. Ashiba. (2023). Novel proposed technique for automatic fabric defect detection. Multimedia Tools and Applications, 1-24. [CrossRef]
    [8] Paulauskaite-Taraseviciene, Agne, Eimantas Noreika, Ramunas Purtokas, Ingrida Lagzdinyte-Budnike, Vytautas Daniulaitis, and Ruta Salickaite-Zukauskiene. (2022). An intelligent solution for automatic garment measurement using image recognition technologies. Applied Sciences, 12(9), 4470. [CrossRef]
    [9] Shahrabadi, Somayeh, Yusbel Castilla, Miguel Guevara, Luís G. Magalhães, Dibet Gonzalez, and Telmo Adão. (2022). Defect detection in the textile industry using image-based machine learning methods: a brief review. In Journal of Physics: Conference Series, IOP Publishing, 2224(1), 012010. [CrossRef]
    [10] Kim, Hyungjung, Woo-Kyun Jung, Young-Chul Park, Jae-Won Lee, and Sung-Hoon Ahn. (2022). Broken stitch detection method for sewing operation using CNN feature map and image-processing techniques. Expert Systems with Applications, 188, 116014. [CrossRef]
    [11] Ahilan, A., Manogaran, G., Raja, C., Kadry, S., Kumar, S.N., Kumar, C.A., Jarin, T., Krishnamoorthy, S., Kumar, P.M., Babu, G.C., and Murugan, N.S. (2019). Segmentation by fractional order darwinian particle swarm optimization based multilevel thresholding and improved lossless prediction-based compression algorithm for medical images. IEEE Access, 7, 89570-89580. [CrossRef]
    [12] Mathew, A., Amudha, P., and Sivakumari, S. (2021). Deep learning techniques: an overview. Advanced Machine Learning Technologies and Applications: Proceedings of AMLTA 2020, 599-608. [CrossRef]
    [13] Song, Jianqiang, Xuemei Xie, Guangming Shi, and Weisheng Dong. (2019). Multi-layer discriminative dictionary learning with locality constraint for image classification. Pattern Recognition, 91, 135-146. [CrossRef]
    [14] Jidesh, P., and Febin, I. P. (2019). Estimation of noise using non-local regularization frameworks for image denoising and analysis. Arabian Journal for Science and Engineering, 44, 3425-3437. [CrossRef]
    [15] Tian, Chunwei, Lunke Fei, Wenxian Zheng, Yong Xu, Wangmeng Zuo, and Chia-Wen Lin. (2020). Deep learning on image denoising: An overview. Neural Networks, 131, 251-275. [CrossRef]
    [16] Huang, Yiming, Di Wu, Zhifen Zhang, Huabin Chen, and Shanben Chen. (2017). EMD-based pulsed TIG welding process porosity defect detection and defect diagnosis using GA-SVM. Journal of Materials Processing Technology, 239, 92-102. [CrossRef]
    [17] Archana, M., Kausalya, K., Monisha, C., and Anila. S. (2019). An Intelligent Fabric Defect Inspection and Detection System Using Image Processing.
    [18] Yapi, Daniel, Adama Nouboukpo, Mohand Saïd Allili, and Member, I. E. E. E. (2023). Mixture of multivariate generalized Gaussians for multi-band texture modeling and representation. Signal Processing, 209, 109011. [CrossRef]
    [19] Salem, Yassine Ben, and Mohamed Naceur Abdelkrim. (2020). Texture classification of fabric defects using machine learning. International Journal of Electrical and Computer Engineering, 10(4), 4390. [CrossRef]

Dennise Mathew and N.C Brintha (2024), Deep-GD: Deep Learning based Automatic Garment Defect Detection and Type Classification. IJEER 12(1), 41-47. DOI: 10.37391/IJEER.120107.