False Alarm Reduction in Automatic Manufacturing Quality Control

The patent introduces an intermediary classification system between the initial defect detection and the final decision-making process. This intermediary layer analyzes multiple features (texture, shape, location, size) of detected anomalies and compares them against learned patterns of true defects versus false alarms, thereby mediating between high-sensitivity detection and reliable classification to reduce false alarms while maintaining defect detection capability

The system changes multiple parameters simultaneously including texture features, shape descriptors, location characteristics, and size metrics of detected anomalies. By analyzing changes across multiple parameters rather than relying on a single threshold, the system can distinguish between true defects and false alarms more effectively, resolving the contradiction between detection sensitivity and false alarm rate

The patent applies partial action by selectively performing follow-up actions only on detected anomalies that are classified as true defects with high confidence, rather than taking exhaustive follow-up actions on all detected anomalies. This selective approach maintains thorough defect identification for genuine defects while avoiding unnecessary follow-up actions on false alarms, thereby preserving production efficiency

The system implements feedback by using the results of follow-up actions (operator confirmations, re-inspections) to continuously refine and update the classification model. This feedback loop improves the system's ability to distinguish true defects from false alarms over time, reducing the need for exhaustive follow-up actions and thereby improving production efficiency while maintaining defect identification thoroughness

The system applies self-service by automatically generating training data from production images and initial defect detections without requiring extensive manual labeling. The classification model learns from the patterns it encounters during normal operation, using the available production data to train and refine its ability to distinguish true defects from false alarms, thereby achieving improved classification accuracy without requiring additional labeled training data

The patent performs preliminary action by pre-processing and feature extraction on production images before classification, creating enriched training representations from the available data. This preliminary feature engineering maximizes the information content of limited training data, improving classification accuracy without requiring additional data collection