Malleable pruning meets more scaled wide-area of attention model for real-time crack detection-Reference-Cited by-同舟云学术

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Malleable pruning meets more scaled wide-area of attention model for real-time crack detection

Published:2024-06-17 Issue: Volume: Page:
ISSN:0178-2789
Container-title:The Visual Computer
language:en
Short-container-title:Vis Comput

Author:

Wu Jun,Nie Wanyu,Zheng Yu,Zuo Gan,Dong Jiaming,Wei Siwei

Funder

the National Natural Science Foundation of China

Hubei Province Science and Technology Support Project

the Fundamental Research Funds for the Research Fund of Hubei University of Technology

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00371-024-03522-z.pdf

Reference65 articles.

1. Chen, Z., Zhang, J., Lai, Z., Chen, J., Liu, Z., Li, J.: Geometry-aware guided loss for deep crack recognition. In: Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4693–4702 (2022)

2. Kheradmandi, N., Mehranfar, V.: A critical review and comparative study on image segmentation-based techniques for pavement crack detection. Constr. Build. Mater. 321, 126162 (2022)

3. Hua, W., Li, J., Zhu, Z., Li, A., Huang, J., Gan, Z., Dong, S.: A review of mixed mode I–II fracture criteria and their applications in brittle or quasi-brittle fracture analysis. Theor. Appl. Fract. Mech. 124, 103741 (2023)

4. Kang, C.J., Peter, W.C.H., Siang, T.P., Jian, T.T., Zhaofeng, L., Yu-Hsing, W.: An active learning framework featured Monte Carlo dropout strategy for deep learning-based semantic segmentation of concrete cracks from images. Struct. Health Monit. 22(5), 3320–3337 (2023)

5. Liu, Z., Gu, X., Chen, J., Wang, D., Chen, Y., Wang, L.: Automatic recognition of pavement cracks from combined GPR b-scan and c-scan images using multiscale feature fusion deep neural networks. Autom. Constr. 146, 104698 (2023)

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