An adaptive loss weighting multi-task network with attention-guide proposal generation for small size defect inspection-Reference-Cited by-同舟云学术

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An adaptive loss weighting multi-task network with attention-guide proposal generation for small size defect inspection

Published:2023-03-09 Issue:2 Volume:40 Page:681-698
ISSN:0178-2789
Container-title:The Visual Computer
language:en
Short-container-title:Vis Comput

Author:

Wu Huangyuan,Li Bin^ORCID,Tian Lianfang,Feng Junjian,Dong Chao

Funder

Key-Area Research and Development Program of Guangdong Province

2021 Guangdong Provincial Science and Technology Special Fund

Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province

Laboratory of Autonomous Systems and Network Control of Ministry of Education

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/s00371-023-02809-x.pdf

Reference58 articles.

1. 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

2. Liu, G., Li, F.: Fabric defect detection based on low-rank decomposition with structural constraints. Vis. Comput. 38, 639–653 (2022). https://doi.org/10.1007/s00371-020-02040-y

3. Wang, L., Zhang, Z.: Automatic detection of wind turbine blade surface cracks based on UAV-taken images. IEEE Trans. Ind. Electron. 64, 7293–7303 (2017). https://doi.org/10.1109/tie.2017.2682037

4. Wang, L., Zhang, Z., Xu, J., Liu, R.: Wind turbine blade breakage monitoring with deep autoencoders. IEEE Trans. Smart Grid 9, 2824–2833 (2018). https://doi.org/10.1109/tsg.2016.2621135

5. Wang, L., Zhang, Z., Luo, X.: A two-stage data-driven approach for image-based wind turbine blade crack inspections. IEEE/ASME Trans. Mechatron. 24, 1271–1281 (2019). https://doi.org/10.1109/TMECH.2019.2908233

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