Detection and Classification of Faulty Weft Threads Using Both Feature-Based and Deep Convolutional Machine Learning Methods
In our work, we analyze how faulty weft threads in air-jet weaving machines can be detected using image processing methods. To this end, we design and construct a multi-camera array for automated acquisition of images of relevant machine areas. These images are subsequently fed into a multi-stage image processing pipeline that allows defect detection using a set of different preprocessing and classification methods. Classification is performed using both image descriptors combined with feature-based machine learning algorithms and deep learning techniques implementing fully convolutional neural networks. To analyze the capabilities of our solution, system performance is thoroughly evaluated under realistic production settings. We show that both approaches show excellent detection rates and that by utilizing semantic segmentation acquired from a fully convolutional network we are not only able to detect defects reliably but also classify defects into different subtypes, allowing more refined strategies for defect removal.
- 1.Wada, Y.: Optical weft sensor for a loom (1984) US Patent 4,471,816Google Scholar
- 2.Karayiannis, Y.A., et al.: Defect detection and classification on web textile fabric using multiresolution decomposition and neural networks. In: The 6th IEEE International Conference on Electronics, Circuits and Systems, Proceedings of ICECS 1999, vol. 2, pp. 765–768. IEEE (1999)Google Scholar
- 6.Kopaczka, M., Saggiomo, M., Guettler, M., Gries, T., Merhof, D.: Fully automatic faulty weft thread detection using a camera system and feature-based pattern recognition. In: Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods (ICPRAM), pp. 124–132 (2018)Google Scholar
- 11.Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893. IEEE (2005)Google Scholar
- 14.Bishop, C.M.: Pattern recognition. Mach. Learn. 128, 1–58 (2006)Google Scholar
- 15.Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar