Weak Supervised Asphalt Pavement Segmentation
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-70819-0_20
Reference15 articles.
1. Yang, F., Zhang, L., Yu, S., Prokhorov, D., Mei, X., Ling, H.: Feature pyramid and hierarchical boosting network for pavement crack detection. IEEE Trans. Intell. Transp. Syst. 21, 1525–1535 (2019)
2. Li, J., Liu, T., Wang, X., Yu, J.: Automated asphalt pavement damage rate detection based on optimized GA-CNN. Autom. Constr. 136(104180), 104180 (2022)
3. Que, Y., et al.: Automatic classification of asphalt pavement cracks using a novel integrated generative adversarial networks and improved VGG model. Eng. Struct. 277(115406), 115406 (2023)
4. Li, H., Wang, W., Wang, M., Li, L., Vimlund, V.: A review of deep learning methods for pixel-level crack detection. J. Traffic Transp. Eng. (Engl. Edn.) 9, 945–968 (2022). https://www.sciencedirect.com/science/article/pii/S2095756422001027
5. Zhou, S., Canchila, C., Song, W.: Deep learning-based crack segmentation for civil infrastructure: data types, architectures, and benchmarked performance. Autom. Constr. 146, 104678 (2023)
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