DEEP CASCADED NEURAL NETWORKS FOR AUTOMATIC DETECTION OF STRUCTURAL DAMAGE AND CRACKS FROM IMAGES

10 citations as recorded by crossref.

1. Datasets and processing methods for boosting visual inspection of civil infrastructure: A comprehensive review and algorithm comparison for crack classification, segmentation, and detection G. Yang et al. https://doi.org/10.1016/j.conbuildmat.2022.129226
2. A Method for Detecting Pathologies in Concrete Structures Using Deep Neural Networks J. Nogueira Diniz et al. https://doi.org/10.3390/app13095763
3. 复杂场景下的双编码器裂缝分割方法 方. Fang Zhi et al. https://doi.org/10.3788/LOP251028
4. Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events Y. Bai et al. https://doi.org/10.1177/14759217221083649
5. DcsNet: a real-time deep network for crack segmentation J. Pang et al. https://doi.org/10.1007/s11760-021-02034-w
6. A One-Step Methodology for Identifying Concrete Pathologies Using Neural Networks—Using YOLO v8 and Dataset Review J. Diniz et al. https://doi.org/10.3390/app14104332
7. DFP-Net: A Crack Segmentation Method Based on a Feature Pyramid Network L. Li et al. https://doi.org/10.3390/app14020651
8. Evolutionary Computation Modelling for Structural Health Monitoring of Critical Infrastructure C. Tumrate et al. https://doi.org/10.1007/s11831-022-09845-1
9. Automated post-earthquake structural damage assessment of concrete buildings using a hybrid deep learning and rule-based framework on image datasets A. Türer et al. https://doi.org/10.1016/j.iintel.2026.100208
10. Vision–LiDAR fusion for 3D crack localization and width measurement H. Zhang et al. https://doi.org/10.1016/j.measurement.2026.123185