| Aspect | What the paper offers | |--------|-----------------------| | | Step‑by‑step protocol from image acquisition (controlled lighting, calibrated DSLR or mirrorless camera) → image pre‑processing (lens distortion correction, radiometric calibration) → crack extraction (Canny edge detector + morphological filtering) → quantitative metrics (crack width, length, orientation). | | Validation | Laboratory tests on concrete specimens with known artificial cracks (0.05 mm – 5 mm width) and field tests on a highway bridge. Reported R² = 0.96 for crack‑width measurements against a laser‑profilometer reference. | | Uncertainty analysis | Provides a full error budget (camera pose, pixel resolution, lighting variation) and recommends a minimum ground sampling distance (GSD) of 0.02 mm/pixel for sub‑millimeter accuracy. | | Open‑source code | Authors released a MATLAB toolbox (downloadable from the supplementary material) that implements the whole pipeline; the repository is now mirrored on GitHub: https://github.com/jrcarlson/PhotoCrackTool . | | Reproducibility | All raw images, calibration targets, and measurement data are deposited in the Mendeley Data repository (doi:10.17632/5xw9h8k7j9.1). | | Citations | As of 2024 the paper has been cited > 250 times, often alongside works on UAV‑based crack mapping, deep‑learning crack segmentation, and structural health monitoring (SHM). |
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If you are looking for or need help navigating the official Carlson licensing, it is best to contact Carlson Software directly for demo options or educational licenses. Carlson Photo Capture - Survey Canvas | Aspect | What the paper offers |
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