Feature reused network: a fast segmentation network model for strip steel surfaces defects based on feature reused
Author:
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/s00371-023-03056-w.pdf
Reference57 articles.
1. Hao, R., Lu, B., Cheng, Y., Li, X., Huang, B.: A steel surface defect inspection approach towards smart industrial monitoring. J. Intell. Manuf. 32(7), 1833–1843 (2020). https://doi.org/10.1007/s10845-020-01670-2
2. Zhou, X., Fang, H., Fei, X., Shi, R., Zhang, J.: Edge-aware multi-level interactive network for salient object detection of strip steel surface defects. IEEE Access 9, 149465–149476 (2021). https://doi.org/10.1109/access.2021.3124814
3. Zhou, X., Fang, H., Liu, Z., Zheng, B., Sun, Y., Zhang, J., Yan, C.: Dense attention-guided cascaded network for salient object detection of strip steel surface defects. IEEE Trans. Instrum. Meas. 71, 1–14 (2022). https://doi.org/10.1109/tim.2021.3132082
4. Song, G., Song, K., Yan, Y.: EDRNet: encoder–decoder residual network for salient object detection of strip steel surface defects. IEEE Trans. Instrum. Meas. 69(12), 9709–9719 (2020). https://doi.org/10.1109/tim.2020.3002277
5. Lee, S.Y., Tama, B.A., Moon, S.J., Lee, S.: Steel surface defect diagnostics using deep convolutional neural network and class activation map. Appl. Sci. 9, 9245449 (2019). https://doi.org/10.3390/app9245449
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