Abstract
Fabric defect detection plays an important role in automated inspection and quality control in textile manufacturing. As the textures and defects in fabric images have complexity and diversity, the traditional detection methods show a poor adaptability and low detection accuracy. Low-rank decomposition model that can be used to separate the image into object and background have proven applicable in fabric defect detection. However, how to represent texture feature of the fabric image more effectively is still problematic in this kind of method. Also, in traditional Low-rank decomposition model, we tend to seek the convex surrogate to resolve this model. However, this results in low accuracy and more noises in sparse part. In this paper, a novel fabric defect detection method based on combination of deep global feature and handcrafted local features and NTV-NRPCA is proposed. In this method, image representation ability is well enhanced through fusing the global deep feature extracted by a convolutional neural network and the handcrafted low-level feature masterly. Then, the non-convex total variation regularized non-convex RPCA (NTV-NRPCA) is proposed in which non-convex solution is more approximate to the real solution and non-convex total variation constraint significantly reduces the noises in sparse part. Finally, the defect region is located by segmenting the saliency map generated by the sparse matrix via a threshold segmentation algorithm. The experimental results show that the proposed method improves the adaptability and detection accuracy comparing to the state-of-the-art.
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Acknowledgements
The authors would like to thank Dr. Henry Y.T. Ngan, Industrial Automation Research Laboratory, Dept. of Electrical and Electronic Engineering, The University of Hong Kong, for providing the database of patterned fabric images. This work was supported by National Nature Science Foundation of China (No. 61772576, U1804157), the Key Natural Science Foundation of Henan Province (No. 162300410338), Science and technology innovation talent project of Education Department of Henan Province (17HASTIT019), the Henan Science Fund for Distinguished Young Scholars (184100510002), Henan science and technology innovation team (CXTD2017091), IRTSTHN (18IRTSTHN013), Program for Interdisciplinary Direction Team in Zhongyuan University of Technology.
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Wang, J., Li, C., Liu, Z., Dong, Y., Huang, Y. (2019). Combing Deep and Handcrafted Features for NTV-NRPCA Based Fabric Defect Detection. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11859. Springer, Cham. https://doi.org/10.1007/978-3-030-31726-3_41
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DOI: https://doi.org/10.1007/978-3-030-31726-3_41
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