Fabric defect detection and classification via deep learning-based improved Mask RCNN
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-023-02884-6.pdf
Reference24 articles.
1. Liu, J., Wang, C., Su, H., Du, B., Tao, D.: Multistage GAN for fabric defect detection. IEEE Trans. Image Process. 29, 3388–3400 (2019). https://doi.org/10.1109/TIP.2019.2959741
2. Jing, J., Wang, Z., Rätsch, M., Zhang, H.: Mobile-Unet: an efficient convolutional neural network for fabric defect detection. Text. Res. J. 92(1–2), 30–42 (2022). https://doi.org/10.1177/0040517520928604
3. Ouyang, W., Xu, B., Hou, J., Yuan, X.: Fabric defect detection using activation layer embedded convolutional neural network. IEEE Access 7, 70130–70140 (2019). https://doi.org/10.1109/ACCESS.2019.2913620
4. Jing, J., Zhuo, D., Zhang, H., Liang, Y., Zheng, M.: Fabric defect detection using the improved YOLOv3 model. J. Eng. Fibers Fabr. 15, 1558925020908268 (2020). https://doi.org/10.1177/1558925020908268
5. Jing, J.F., Ma, H., Zhang, H.H.: Automatic fabric defect detection using a deep convolutional neural network. Color. Technol. 135(3), 213–223 (2019). https://doi.org/10.1111/cote.12394
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