Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Tay, Francis Eng Hock
- Fabric Defect Detection Based on Improved Object As Point
Abstract Views :147 |
PDF Views:96
Authors
Affiliations
1 Department of Mechanical Engineering, National University of Singapore, SG
1 Department of Mechanical Engineering, National University of Singapore, SG
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 13, No 3 (2021), Pagination: 1-13Abstract
In the field of fabric manufacturing, many factories still utilise the traditional manual detection method. It requires a lot of labour, resulting in high error rates and low efficiency. In this paper, we represent a real-time automated detection method based on object as point. This work makes three attributions. First, we build a fabric defects database and augment the data to training the intelligence model. Second, we provide a real-time fabric defects detection algorithm, which have potential to be applied in manufacturing. Third, we figure out CenterNet with soft NMS will improved the performance in fabric defect detection area, which is considered an NMS-free algorithm. Experiment results indicated that our lightweight network based method can effectively and efficiently detect five different fabric defects.Keywords
Fabric Defects Detection, Object As Point, Data Augmentation, Deep Learning.Full Text
References
- Ngan, H. Y., Pang, G. K., & Yung, N. H. (2011). Automated fabric defect detection—a review. Image and vision computing, 29(7), 442-458.
- Srinivasan, K., Dastoor, P. H., Radhakrishnaiah, P., & Jayaraman, S. (1992). FDAS: A knowledge-based framework for analysis of defects in woven textile structures. Journal of the textile institute, 83(3), 431-448.
- Liu, Z., Liu, X., Li, C., Li, B., & Wang, B. (2018, April). Fabric defect detection based on faster R-CNN. In Ninth International Conference on Graphic and Image Processing (ICGIP 2017) (Vol. 10615, p. 106150A). International Society for Optics and Photonics.
- YUAN, L. H., Fu, L., Yang, Y., & Miao, J. (2009). Analysis of texture feature extracted by gray level co-occurrence matrix [J]. Journal of Computer Applications, 29(4), 1018-1021.
- Youting, Z. X. D. Z. R. (2012). Detection of Fabric Defect Based on Weighted Local Entropy [J]. Cotton Textile Technology, 10.
- Arivazhagan, S., & Ganesan, L. (2003). Texture classification using wavelet transform. Pattern recognition letters, 24(9-10), 1513-1521.
- ZHOU, S., ZHANG, F. S., & LI, F. C. (2013). Image Feature Extraction of Fabric Defect Based on Wavelet Transform [J]. Journal of Qingdao University (Engineering & Technology Edition), 2.
- Kumar, A., & Pang, G. K. (2002). Defect detection in textured materials using Gabor filters. IEEE Transactions on industry applications, 38(2), 425-440.
- Xiaobo, Y. (2013). Fabric defect detection of statistic aberration feature based on GMRF model. Journal of Textile Research, 4, 026.
- Mak, K. L., Peng, P., & Lau, H. Y. K. (2005, December). A real-time computer vision system for detecting defects in textile fabrics. In 2005 IEEE International Conference on Industrial Technology (pp. 469-474). IEEE.
- Jeyaraj, P. R., & Nadar, E. R. S. (2019). Computer vision for automatic detection and classification of fabric defect employing deep learning algorithm. International Journal of Clothing Science and Technology.
- Zhou, X., Wang, D., & Krähenbühl, P. (2019). Objects as points. arXiv preprint arXiv:1904.07850.
- Hanbay, K., Talu, M. F., & Özgüven, Ö. F. (2016). Fabric defect detection systems and methods—A systematic literature review. Optik, 127(24), 11960-11973.
- Chaitanya, K., Karani, N., Baumgartner, C. F., Becker, A., Donati, O., & Konukoglu, E. (2019, June). Semi-supervised and task-driven data augmentation. In International conference on information processing in medical imaging (pp. 29-41). Springer, Cham.
- Leng, B., Yu, K., & Jingyan, Q. I. N. (2017). Data augmentation for unbalanced face recognition training sets. Neurocomputing, 235, 10-14.
- DeVries, T., & Taylor, G. W. (2017). Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552.
- Newell, A., Yang, K., & Deng, J. (2016, October). Stacked hourglass networks for human pose estimation. In European conference on computer vision (pp. 483-499). Springer, Cham.
- Bodla, N., Singh, B., Chellappa, R., & Davis, L. S. (2017). Soft-NMS--improving object detection with one line of code. In Proceedings of the IEEE international conference on computer vision (pp. 5561-5569).