Enhancing image stitching for civil engineering applications: A hyperparameter selection approach using regression analysis of errors

Abstract

Image stitching is a widely utilized computer vision technique with applications in panorama generation, virtual reality, 3D reconstruction, and structural inspection. Accurate estimation of the homography matrix is critical for stitching quality but remains sensitive to hyperparameter selection and lacks systematic optimization frameworks. This study proposes a regression analysis-based hyperparameter optimization algorithm to ensure stable control over stitching errors. Quantitative variables characterizing the number and spatial distribution of inliers were defined, and a regression model was developed to predict stitching errors based on these characteristics. Bayesian optimization was then employed to determine optimal hyperparameters. Experimental validation using 100 high-resolution drone-captured image pairs demonstrated significant improvements. The proposed algorithm reduced average stitching error by 21.4% and maximum error by 61.7%, effectively eliminating critical failures exceeding 1.5%. Visual comparisons confirmed consistent improvements in alignment quality across diverse cases. This research introduces an innovative “error prediction-optimization” framework for hyperparameter tuning, providing a robust foundation for reliable image stitching in applications such as drone-based inspections and virtual reality mapping. Future work will extend validation to challenging imaging conditions and explore lightweight optimization methods for real-time processing.