Analysis of Bayesian Hyperparameter Optimization Results in Deep Neural Networks for Wide-Angle Camera Geometric Characterization

This paper addresses the challenge of geometric camera characterization, a crucial process in various applications such as computer vision, aerial photogrammetry, remote sensing, and robotics. Traditional calibration methods rely on calibration targets or manually defined geometric structures, which limit automation and adaptability, especially in uncontrolled environments. To overcome these limitations, we propose an innovative approach based on deep learning, capable of estimating the camera’s intrinsic parameters directly from a single image. The developed method integrates the Optuna hyperparameter optimizer, which utilizes Bayesian optimization to enhance model accuracy while reducing computational cost and training time. The application of this approach accelerates the search for optimal configurations for neural networks, ensuring an efficient balance between performance and architectural complexity. Experimental results demonstrate a significant improvement in model accuracy, with mean absolute errors and standard deviations in distortion rates at the hundredth-order magnitude level and focal length determination below 6 mm. Compared to the model without the integrated optimizer, there was an over 95% gain in reducing Mean Squared Error (MSE) and an 82% reduction in the standard deviation. This research significantly contributes to the enhancement of autonomous navigation and image-based positioning systems, providing a scalable and automated alternative to conventional modeling techniques (SDE).