PaveSAM – segment anything for pavement distress
Author:
Affiliation:
1. Civil and Environmental Engineering Department, University of Missouri-Columbia, Columbia, MO, USA
2. Civil and Environmental Engineering, North Dakota State University, Fargo, ND, USA
Funder
National
Science
Foundation
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/14680629.2024.2374863
Reference59 articles.
1. Bucher M. Vu T.-H. Cord M. & Pérez P. (2019). Zero-shot semantic segmentation. arXiv:1906.00817.
2. LinkNet: Exploiting encoder representations for efficient semantic segmentation
3. Deeplab: Semantic image segmentation with deep convolutional nets;Chen L.-C.;Atrous Convolution, and Fully Connected CRFs,2016
4. Chen L.-C. Zhu Y. Papandreou G. Schroff F. & Adam H. (2018). Encoder–decoder with atrous separable convolution for semantic image segmentation. arXiv:1802.02611.
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