Modelling and Estimation of Interior Orientation of Non-Metric Cameras using Artificial Intelligence

With the advancement of low-cost digital cameras and their widespread, especially in smartphones, these cameras are not designed for photogrammetry applications. The main aim of this study is to model and estimate the interior orientation parameters (IOPs) for images captured by volunteer-run cameras using artificial intelligence. These cameras were unavailable to perform traditional calibration processes or use images from unknown sources for image measuring. Estimating IOPs using random values within the range determined by testing the selected sample's consistency. Optimization was performed by Utilizing the Simulated Annealing algorithm based on stereo calibration to obtain the best possible values of these parameters that produce the minimum RMS-reprojection error attained. By The variance between the parameters from the pre-calibration process and estimated by an artificial intelligence system, The coefficient of determination in the IOPs (focal length R2 = 0.717 to 0.812, principal point (X, Y) R2 = (0.674 to 0.869, 0.504 to 0.613), Both radial and tangential coefficients, R2 was close to zero). Therefore, the estimations of radial distortions k1, k2, and tangential distortions p1 and p2 are invalid. A reasonably strong relationship between principal distance and principal point with low lens distortion parameters due to the significant relative differences between the distortion parameters, sufficient strength of the relationship between calculating parameters, and estimating according to accuracy tolerance in photogrammetry applications.