Research on Fabric Defect Detection Technology Based on RDN-LTE and Improved DINO
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-50075-6_11
Reference18 articles.
1. Selvi, S.S.T., Nasira, G.: An effective automatic fabric defect detection system using digital image processing. J. Environ. Nanotechnol. 6(1), 79–85 (2017)
2. Chen, Z., Qiu, J., Sheng, B., Li, P., Wu, E.: GPSD: generative parking spot detection using multi-clue recovery model. Vis. Comput. 37(9–11), 2657–2669 (2021). https://doi.org/10.1007/s00371-021-02199-y
3. Li, J., et al.: Automatic detection and classification system of domestic waste via multimodel cascaded convolutional neural network. IEEE Trans. Ind. Inf. 18(1), 163–173 (2021)
4. Liu, G., Li, F.: Fabric defect detection based on low-rank decomposition with structural constraints. Vis. Comput. 38(2), 639–653 (2022). https://doi.org/10.1007/s00371-020-02040-y
5. Xu, Y., Meng, F., Wang, L., Zhang, M., Wu, C.: Fabric surface defect detection based on GMRF model. In: 2021 2nd International Conference on Artificial Intelligence and Information Systems, pp. 1–4 (2021)
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