Abstract
In order to solve the problems of various kinds of defects, defect ratio varies greatly, imbalanced defect aspect ratio and high integration degree with background in fabric defect detection, a method combining super-resolution reconstruction technology and deep learning detection was proposed. Firstly, the enhanced deep residual networks for single image super-resolution is used to enrich the defect feature information, reduce the fusion degree of defect and background texture, and enhance the extraction ability of various defect features. Then, the defect features are analyzed according to K-means clustering algorithm. Based on the three default anchor frame ratios provided by Faster RCNN, six new types of anchor ratios are added. Then, FPN module and DCNv2 module were introduced in Faster RCNN to improve the ability to identify defects with different areas and shapes. Finally, the pooling mode of ROI layer was modified to eliminate the error caused by quantization operation. The results of the three kinds of comparative experiments show that the method based on EDSR and improved Faster RCNN has a better overall recognition rate for multiple kinds of fabric defects than other current methods, and can be used in the production and operation of textile enterprises.
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References
Stojanovic, R., Mitropulos, P., Koulamas, C., Karayiannis, Y., Koubias, S., Papadopoulos, G.: Real-time vision-based system for textile fabric inspection. Real-Time Imaging 7, 507–518 (2001)
Banumathi, P., Nasira, G.M.: Artificial neural network techniques in identifying plain woven fabric defects. Res. J. Appl. Sci. Eng. Technol. 9(4), 272–276 (2015)
Tola, S., Sarkar, S., Chandra, J.K., Sarkar, G.: Sparse auto-encoder improvised texture-based statistical feature estimation for the detection of defects in woven fabric. In: Chakraborty, M., Jha, R.K., Balas, V.E., Sur, S.N., Kandar, D. (eds.) Trends in Wireless Communication and Information Security. LNEE, vol. 740, pp. 143–151. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-6393-9_16
Tang, X., Huang, K., Qin, Y.: Fabric defect detection based on Gabor Filter and HOG. Comput. Measur. Control 26(9), 39–42 (2018)
Tong, L., Wong, W.K., Kwong, C.K.: Differential evolution-based optimal Gabor filter model for fabric inspection. Neurocomputing 173, 1386–1401 (2016)
Xu, Y., Meng, F., Wang, L.: Fabric surface defect detection based on GMRF Model. In: International Conference on Artificial Intelligence and Information Systems, pp. 1–4 (2021)
Wang, Y., Hao, Z., Zuo, F., Su, Z.: Fabric defect target detection algorithm based on YOLOv4 improvement. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds.) WISA 2021. LNCS, vol. 12999, pp. 647–658. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87571-8_56
Xie, J., Wang, W., Liu, T.: Fabric surface defect detection based on YOLO v3 with different backbone networks. Measur. Control Technol. 40(3), 61–66 (2021)
Liu, Z., Liu, S., Li, C., Ding, S., Dong, Y.: Fabric defects detection based on SSD. In: Proceedings of the 2nd International Conference on Graphics and Signal Processing, pp. 74–78 (2018)
Wei, B., Hao, K., Tang, X.-S., Ren, L.: Fabric defect detection based on faster RCNN. In: Wong, W.K. (ed.) AITA 2018. AISC, vol. 849, pp. 45–51. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-99695-0_6
An, J., Tang, Y., Ma, X.: Defect detection algorithm of plain cloth based on deep neural network. Packag. Eng. 42(3), 246–251 (2021)
Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: The IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136–144 (2017)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
Zhu, X., Hu, H., Lin, S., Dai, J.: Deformable ConvNets v2: more deformable, better results. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9308–9316 (2019)
Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154–6162 (2018)
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Yao, L., Zhang, N., Gao, A., Wan, Y. (2022). Research on Fabric Defect Detection Technology Based on EDSR and Improved Faster RCNN. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13370. Springer, Cham. https://doi.org/10.1007/978-3-031-10989-8_38
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