A COST-EFFECTIVE METHOD FOR CRACK DETECTION AND MEASUREMENT ON CONCRETE SURFACE

9 citations as recorded by crossref.

1. A performance-driven evaluation of deep learning for concrete crack detection with varying dataset sizes and training epochs: real-world implications for infrastructure monitoring S. Bussa et al. https://doi.org/10.1007/s42107-025-01361-4
2. Evaluation of the low-cost depth cameras for non-destructive testing N. Alghanim et al. https://doi.org/10.3846/gac.2025.20152
3. Enhanced ResNet50 deep learning algorithm for classification of crack images in RCC structures S. Bussa & N. Boppana https://doi.org/10.1007/s42107-025-01396-7
4. Proposing UGV and UAV Systems for 3D Mapping of Orchard Environments A. Tagarakis et al. https://doi.org/10.3390/s22041571
5. YOLOv8-CDD: an improved concrete defect detection method combined CNN with transformer C. Wang et al. https://doi.org/10.1088/1361-6501/ad85f5
6. Stereo-vision-based 3D concrete crack detection using adversarial learning with balanced ensemble discriminator networks S. Shim et al. https://doi.org/10.1177/14759217221097868
7. Crack detection system in AWS Cloud using Convolutional neural networks G. Coca et al. https://doi.org/10.1016/j.procs.2020.08.041
8. A Decade of Modern Bridge Monitoring Using Terrestrial Laser Scanning: Review and Future Directions M. Rashidi et al. https://doi.org/10.3390/rs12223796
9. VisionCrack: Smartphone‐Based Fine Crack Width Measurement via Adaptive Scaling With Multithickness 1D Barcodes R. Raupov et al. https://doi.org/10.1155/stc/7069645