AI Image-Recognition System for AOI False Positive Reduction

The system performs preliminary actions by collecting historical inspection data and artificially generating defective samples before the actual inspection process. This pre-preparation of diverse training data enables the AI model to learn from a wide range of scenarios, improving its ability to distinguish true defects from normal variations without requiring overly strict judgement conditions during production inspection.

The system implements feedback mechanisms by continuously evaluating AI model performance using reserved test datasets and manually verified ground truth data. The model is retrained periodically with updated data, creating a closed-loop system that progressively improves detection accuracy while reducing false positives, thus balancing reliability and precision over time.

The AI inspection system performs self-service by automatically learning from historical data and improving its own performance through continuous retraining. The system autonomously handles the inspection process without requiring constant manual intervention or verification, as the AI model progressively becomes more accurate in distinguishing defects from normal variations, thereby maintaining high reliability while preserving productivity.

By pre-training the AI model with extensively curated and manually verified defect data before deployment, the system performs preliminary work that reduces the need for manual verification during actual production inspection. This advance preparation enables the model to handle a wide variety of defect scenarios autonomously, improving reliability without sacrificing throughput.

The system performs preliminary action by artificially generating defective samples and balancing the training data distribution before model training begins. This pre-balancing ensures that the AI model receives equitable exposure to different defect types during training, preventing bias toward majority classes and improving overall detection accuracy without significantly extending training time.

The system applies parameter changes by adjusting the data distribution parameters to achieve balance across different defect categories. By modifying the composition of training datasets to ensure representative sampling of all defect types, the model learns more accurate decision boundaries for each category, improving measurement precision while maintaining efficient training through optimized data selection.