Multi-dimensional feature extraction-based deep encoder–decoder network for automatic surface defect detection
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-07885-z.pdf
Reference65 articles.
1. Dong H, Song K, He Y et al (2020) PGA-Net: pyramid feature fusion and global context attention network for automated surface defect detection. IEEE Trans Industr Inf 16:7448–7458. https://doi.org/10.1109/TII.2019.2958826
2. Song G, Song K, Yan Y (2020) EDRNet: encoder-decoder residual network for salient object detection of strip steel surface defects. IEEE Trans Instrum Meas 69:9709–9719. https://doi.org/10.1109/TIM.2020.3002277
3. Xian-guang F, Xiao-dong W, Yu-xin C, Xin W (2019) Image processing for three defects of topography images by SPM. Chemom Intell Lab Syst 185:12–17. https://doi.org/10.1016/j.chemolab.2018.12.013
4. Zhang D, Song K, Xu J et al (2021) MCnet: multiple context information segmentation network of no-service rail surface defects. IEEE Trans Instrum Meas 70:1–9. https://doi.org/10.1109/TIM.2020.3040890
5. Uzen H, Turkoglu M, Hanbay D (2021) Texture defect classification with multiple pooling and filter ensemble based on deep neural network. Expert Syst Appl 175:114838. https://doi.org/10.1016/j.eswa.2021.114838
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