Depth-wise Squeeze and Excitation Block-based Efficient-Unet model for surface defect detection
Author:
Funder
Inönü Üniversitesi
Publisher
Springer Science and Business Media LLC
Subject
Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s00371-022-02442-0.pdf
Reference89 articles.
1. Dong, H., Song, K., He, Y., Xu, J., Yan, Y., Meng, Q.: PGA-Net: pyramid feature fusion and global context attention network for automated surface defect detection. IEEE Trans. Industr. Inf. 16, 7448–7458 (2020). https://doi.org/10.1109/TII.2019.2958826
2. Huang, Y., Qiu, C., Yuan, K.: Surface defect saliency of magnetic tile. Vis. Comput. 36, 85–96 (2020). https://doi.org/10.1007/s00371-018-1588-5
3. Uzen, H., Turkoglu, M., Hanbay, D.: Texture defect classification with multiple pooling and filter ensemble based on deep neural network. Expert Syst. Appl. 175, 114838 (2021). https://doi.org/10.1016/j.eswa.2021.114838
4. Hu, W., Wang, T., Wang, Y., Chen, Z., Huang, G.: LE–MSFE–DDNet: a defect detection network based on low-light enhancement and multi-scale feature extraction. Vis. Comput. (2021). https://doi.org/10.1007/s00371-021-02210-6
5. Uzen, H., Firat, H., Karci, A., Hanbay, D.: Automatic thresholding method developed with entropy for fabric defect detection. In: 2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019. Institute of Electrical and Electronics Engineers Inc. (2019)
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