AI Fiducial Marker Detection for Stable Reference Point Alignment

The patent replaces traditional machine vision algorithms with an artificial intelligence module that uses deep learning neural networks to predict fiducial marker locations. This substitution of the recognition system achieves higher detection consistency and lower variance by learning from training data rather than relying on conventional image processing methods.

The patent implements a training phase before actual fiducial detection where the AI module is trained with labeled image data containing fiducial markers. This preliminary training action enables the system to achieve reliable and consistent detection performance in subsequent operations by pre-learning the characteristics of fiducial markers under various conditions.

The patent changes the fundamental parameter of the detection system by transitioning from traditional image processing algorithms to an AI-based prediction system. This parameter change allows the system to achieve high detection accuracy without requiring extensive fine-tuning of fiducial manufacturing parameters, thereby reducing setup and evaluation costs.

The AI module performs self-learning through the training phase where it automatically adjusts its internal parameters and weights to optimize fiducial detection. This self-service capability eliminates the need for manual fine-tuning of process parameters and materials, reducing labor force requirements and manufacturing costs.