Image Masking for Synthetic Training Data Generation

The patent applies the copying principle by generating synthetic training images that replicate real images through image synthesis technology. The system creates virtual images that mimic real-world objects, scenes, and variations, thereby reproducing the diversity needed for training without requiring actual physical image collection. This resolves the contradiction by providing sufficient training data (improving model recognition ability) while eliminating the time-consuming manual collection process

The patent replaces the mechanical manual collection process with an automated image synthesis system. Instead of physically gathering images from various sources, the system uses computational algorithms to generate images programmatically. This substitution of mechanical human activity with automated digital processes eliminates the time loss while maintaining the quality and diversity of training data

The patent applies the dynamics principle by implementing a system that can generate diverse images dynamically through parameter adjustment. The image synthesis system allows for real-time variation in image characteristics such as lighting conditions, object positions, angles, and environmental factors. This dynamic generation capability enables the system to create an unlimited variety of training images on demand, achieving both high diversity and high efficiency simultaneously

The patent utilizes parameter changes by modifying synthesis parameters (such as image resolution, color distribution, object orientation, and background characteristics) to generate diverse training images from a single base image or model. By systematically varying these parameters, the system produces a comprehensive dataset with diverse characteristics without requiring manual collection, thereby improving both diversity and efficiency