Multi-stage unsupervised fabric defect detection based on DCGAN (original ) (raw ) References
Bao, X., Liang, J., Xia, Y., Hou, Z., Huan, Z.: Low-rank decomposition fabric defect detection based on prior and total variation regularization. Vis. Comput. 38 (8), 2707–2721 (2022). https://doi.org/10.1007/s00371-021-02148-9 Article Google Scholar
Hanbay, K., Talu, M.F., Özgüven, Ö.F.: Fabric defect detection systems and methodsa systematic literature review. Optik 127 (24), 11960–11973 (2016). https://doi.org/10.1016/j.ijleo.2016.09.110 Article Google Scholar
Ngan, H.Y., Pang, G.K., Yung, N.H.: Automated fabric defect detection-a review. Image Vis. Comput. 29 (7), 442–458 (2011). https://doi.org/10.1016/j.imavis.2011.02.002 Article Google Scholar
Li, C., Li, J., Li, Y., He, L., Fu, X., Chen, J.: Fabric defect detection in textile manufacturing: a survey of the state of the art. Secur. Commun. Netw. (2021). https://doi.org/10.1155/2021/9948808 Article Google Scholar
Abouelela, A., Abbas, H.M., Eldeeb, H., Wahdan, A.A., Nassar, S.M.: Automated vision system for localizing structural defects in textile fabrics. Pattern Recogn. Lett. 26 (10), 1435–1443 (2005). https://doi.org/10.1016/j.patrec.2004.11.016 Article Google Scholar
Deotale, N.T., Sarode, T.K.: Fabric defect detection adopting combined GLCM, Gabor wavelet features and random decision forest. 3D Res. 10 (1), 1–13 (2019). https://doi.org/10.1007/s13319-019-0215-1 Article Google Scholar
Karlekar, V.V., Biradar, M., Bhangale, K.: Fabric defect detection using wavelet filter. In: 2015 International Conference on Computing Communication Control and Automation, pp. 712–715. IEEE (2015). https://doi.org/10.1109/ICCUBEA.2015.145
Tsang, C.S., Ngan, H.Y., Pang, G.K.: Fabric inspection based on the Elo rating method. Pattern Recogn. 51 , 378–394 (2016). https://doi.org/10.1016/j.patcog.2015.09.022 Article Google Scholar
Cao, J., Wang, N., Zhang, J., Wen, Z., Li, B., Liu, X.: Detection of varied defects in diverse fabric images via modified RPCA with noise term and defect prior. Int. J. Cloth. Sci. Technol. 28 (4), 516–529 (2016). https://doi.org/10.1108/IJCST-10-2015-0117 Article Google Scholar
Li, C., Gao, G., Liu, Z., Huang, D., Xi, J.: Defect detection for patterned fabric images based on GHOG and low-rank decomposition. IEEE Access 7 , 83962–83973 (2019). https://doi.org/10.1109/ACCESS.2019.2925196 Article Google Scholar
Shi, B., Liang, J., Di, L., Chen, C., Hou, Z.: Fabric defect detection via low-rank decomposition with gradient information. IEEE Access 7 , 130423–130437 (2019). https://doi.org/10.1109/ACCESS.2019.2939843 Article Google Scholar
Ji, X., Liang, J., Di, L., Xia, Y., Hou, Z., Huan, Z., Huan, Y.: Fabric defect fetection via weighted low-rank decomposition and Laplacian regularization. J. Eng. Fibers Fabr. 15 , 1558925020957654 (2020). https://doi.org/10.1177/1558925020957654 Article Google Scholar
Jing, J., Wang, Z., Rätsch, M., Zhang, H.: Mobile-Unet: an efficient convolutional neural network for fabric defect detection. Text. Res. J. 92 (1–2), 30–42 (2022). https://doi.org/10.1177/0040517520928604 Article Google Scholar
Liu, J., Wang, C., Su, H., Du, B., Tao, D.: Multistage GAN for fabric defect detection. IEEE Trans. Image Process. 29 , 3388–3400 (2019). https://doi.org/10.1109/TIP.2019.2959741 Article MATH Google Scholar
Li, Y., Zhao, W., Pan, J.: Deformable patterned fabric defect detection with fisher criterion-based deep learning. IEEE Trans. Autom. Sci. Eng. 14 (2), 1256–1264 (2016). https://doi.org/10.1109/TASE.2016.2520955 Article Google Scholar
Mei, S., Wang, Y., Wen, G.: Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model. Sensors 18 (4), 1064 (2018). https://doi.org/10.3390/s18041064 Article Google Scholar
Hu, G., Huang, J., Wang, Q., Li, J., Xu, Z., Huang, X.: Unsupervised fabric defect detection based on a deep convolutional generative adversarial network. Text. Res. J. 90 (3–4), 247–270 (2020). https://doi.org/10.1177/0040517519862880 Article Google Scholar
Cheng, Z., Liang, J., Choi, H., Tao, G., Cao, Z., Liu, D., Zhang, X.: Physical attack on monocular depth estimation with optimal adversarial patches. In: European Conference on Computer Vision, pp. 514–532. Springer (2022). https://doi.org/10.1007/978-3-031-19839-7_30
Yan, L., Ma, S., Wang, Q., Chen, Y., Zhang, X., Savakis, A., Liu, D.: Video captioning using global-local representation. IEEE Trans. Circuits Syst. Video Technol. (2022). https://doi.org/10.1109/TCSVT.2022.3177320 Article Google Scholar
Cui, Y., Cao, Z., Xie, Y., Jiang, X., Tao, F., Chen, Y.V., Li, L., Liu, D.: Dg-labeler and dgl-mots dataset: Boost the autonomous driving perception. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 58–67 (2022)
Liu, D., Cui, Y., Yan, L., Mousas, C., Yang, B., Chen, Y.: Densernet: Weakly supervised visual localization using multi-scale feature aggregation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 6101–6109 (2021). https://doi.org/10.1609/aaai.v35i7.16760
Habib, M.T., Shuvo, S.B., Uddin, M.S., Ahmed, F.: Automated textile defect classification by bayesian classifier based on statistical features. In: 2016 International Workshop on Computational Intelligence (IWCI), pp. 101–105. IEEE (2016). https://doi.org/10.1109/IWCI.2016.7860347
Raheja, J.L., Kumar, S., Chaudhary, A.: Fabric defect detection based on GLCM and Gabor filter: A comparison. Optik 124 (23), 6469–6474 (2013). https://doi.org/10.1016/j.ijleo.2013.05.004 Article Google Scholar
Shumin, D., Zhoufeng, L., Chunlei, L.: Adaboost learning for fabric defect detection based on hog and svm. In: 2011 International conference on multimedia technology, pp. 2903–2906. IEEE (2011). https://doi.org/10.1109/ICMT.2011.6001937
Zhu, D., Pan, R., Gao, W., Zhang, J.: Yarn-dyed fabric defect detection based on autocorrelation function and GLCM. Autex Res. J. 15 (3), 226–232 (2015)Article Google Scholar
Raheja, J.L., Ajay, B., Chaudhary, A.: Real time fabric defect detection system on an embedded DSP platform. Optik 124 (21), 5280–5284 (2013). https://doi.org/10.1016/j.ijleo.2013.03.038 Article Google Scholar
Hou, X., Zhang, L.: Saliency detection: A spectral residual approach. In: 2007 IEEE Conference on computer vision and pattern recognition, pp. 1–8. IEEE (2007). https://doi.org/10.1109/CVPR.2007.383267
Hu, G.H., Wang, Q.H.: Fabric defect detection via un-decimated wavelet decomposition and gumbel distribution model. J. Eng. Fibers Fabr. 13 (1), 155892501801300100 (2018). https://doi.org/10.1177/155892501801300103 Article MathSciNet Google Scholar
Kang, X., Zhang, E.: A universal and adaptive fabric defect detection algorithm based on sparse dictionary learning. IEEE Access 8 , 221808–221830 (2020). https://doi.org/10.1109/ACCESS.2020.3041849 Article Google Scholar
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63 (11), 139–144 (2020). https://doi.org/10.1177/155892501801300103 Article MathSciNet Google Scholar
Wu, Q., Chen, Y., Meng, J.: Dcgan-based data augmentation for tomato leaf disease identification. IEEE Access 8 , 98716–98728 (2020). https://doi.org/10.1109/ACCESS.2020.2997001 Article Google Scholar
Li, M., Tang, H., Chan, M.D., Zhou, X., Qian, X.: DC-AL GAN: pseudoprogression and true tumor progression of glioblastoma multiform image classification based on DCGAN and AlexNet. Med. Phys. 47 (3), 1139–1150 (2020). https://doi.org/10.1002/mp.14003 Article Google Scholar
Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54 , 30–44 (2019). https://doi.org/10.1016/j.media.2019.01.010 Article Google Scholar
Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: International conference on information processing in medical imaging, pp. 146–157. Springer (2017). https://doi.org/10.1007/978-3-319-59050-9_12
Xing, P., Sun, Y., Li, Z.: Self-supervised guided segmentation framework for unsupervised anomaly detection. arXiv preprint arXiv:2209.12440 (2022)
Li, Z., Sun, Y., Zhang, L., Tang, J.: Ctnet: context-based tandem network for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. (2021). https://doi.org/10.1109/TPAMI.2021.3132068 Article Google Scholar
Xing, P., Li, Z.: Visual anomaly detection via partition memory bank module and error estimation. arXiv preprint arXiv:2209.12441 (2022). https://doi.org/10.48550/arXiv.2209.12441
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015). https://doi.org/10.48550/arXiv.1511.06434
Shi, W., Wang, W., Zhu, L., Wu, K., Wu, J.: Clustering-based cycle Gan for fabric defect detection. Soc Sci Electron Publ. (2022). https://doi.org/10.2139/ssrn.4061500
Cui, Y., Yan, L., Cao, Z., Liu, D.: Tf-blender: Temporal feature blender for video object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8138–8147 (2021)
Liu, D., Cui, Y., Chen, Y., Zhang, J., Fan, B.: Video object detection for autonomous driving: motion-aid feature calibration. Neurocomputing 409 , 1–11 (2020). https://doi.org/10.1016/j.neucom.2020.05.027 Article Google Scholar
Liu, D., Cui, Y., Tan, W., Chen, Y.: Sg-net: Spatial granularity network for one-stage video instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9816–9825 (2021)
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