Accuracy Enhancement of Homography-Based Crack Width Calculation Using RGB-D Sensors

Accurate crack width measurement is important for structural condition assessment, but image-based methods are sensitive to oblique viewing angles and varying imaging distances. To address this challenge, this study proposes a method and evaluation framework for crack width measurement under non-orthogonal imaging conditions using RGB-D sensors. The proposed method integrates plane fitting and homography-based geometric rectification to transform imaged cracks into standard orthogonal viewpoints. It then applies dynamic masking and hybrid global–local binarization to the rectified image to improve measurement accuracy and robustness. Finally, this study develops an evaluation framework for comparing the proposed and baseline methods under different viewing angles and imaging distances. The framework establishes correspondences between physical locations along the same crack across RGB-D images captured under different imaging conditions, enabling quantitative analysis of performance variations. Experiments on two cracks in concrete buildings show that the proposed method outperforms the baseline method without geometric rectification, reducing the fitted surface error by 19.3–52.3% while maintaining a validity rate above 99%. The results indicate that incorporating surface geometry offers a practical pathway for quantitative crack assessment in close-range image-based inspection using handheld or UAV-mounted RGB-D cameras.