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Camera calibration is a critical process in computer vision applications, ensuring accurate mapping between image coordinates and real-world coordinates. We propose a method to fine-tune the calibration parameters of cameras by using machine learning technology, aiming to improve the calibration accuracy and stability. The proposed method involves data collection, data preprocessing, dataset division, model selection, model training, model evaluation, parameter refinement, and iterative optimization. Real-world data is collected and pre-processed to ensure data quality and consistency. The dataset is then divided into training and test sets for model training and performance evaluation. A machine learning model using linear regression to model the relationship between camera parameters and calibration errors. The model is trained and evaluated, and the grid search is used for cross-validation to adjust the parameters of the model to make the model better. Based on the model completed by parameter tuning, new camera parameters are input, fine-tuned by genetic algorithm, and the average error is calculated until the set error is satisfied, and the adjusted parameters are output. Finally, the HALCON measurement assistant is used to verify the output results. The results show that although the calibration data obtained by HALCON is not excellent, the calibration accuracy and stability can be improved by using the method in this paper. The research results can be used to optimize the calibration parameters of tilt-shift lenses, which is very helpful for 2D data measurement and 3D reconstruction.
Zhang et al. (Fri,) studied this question.