Auto Qualification Device Using Detection Learning Models

The patent transforms the detection approach from rule-based processing to learning model-based processing. The learning model is trained on diverse training data including various defect types and normal products, enabling it to adaptively learn complex defect patterns rather than relying on fixed rules. This parameter change from deterministic rules to probabilistic learning significantly improves detection precision while maintaining automation.

The patent implements preliminary action by training the learning model extensively before actual defect detection. The model undergoes training with labeled training data containing various defect scenarios, preparing it to handle diverse real-world cases. This preliminary training phase ensures the model is ready to accurately detect defects without requiring complex runtime rule adjustments.

The patent replaces the mechanical human visual inspection system with an automated learning model-based system. The learning model processes images computationally, eliminating the need for human operators to manually examine each product. This substitution maintains high detection accuracy through trained model recognition while dramatically improving productivity by enabling rapid automated processing of multiple products simultaneously.

The patent creates a virtual copy of human expert detection capability through the learning model. By training the model on extensive labeled data including various defect patterns, it replicates and enhances human inspection expertise. This copying approach allows the system to achieve human-level or superior detection accuracy while operating at machine speed, resolving the contradiction between precision and productivity.

The patent applies segmentation by dividing the detection process into focused regions around detected target objects rather than analyzing entire images uniformly. The learning model first identifies potential defect locations, then concentrates detailed analysis on these specific regions. This segmentation approach maintains high detection precision for critical areas while reducing overall processing time by avoiding exhaustive analysis of entire large images.

The patent implements local quality by applying different analysis intensities to different regions. High-resolution detailed analysis is applied locally to regions containing target objects where defects are likely, while other regions receive minimal or no processing. This local quality approach ensures accurate defect detection where needed while minimizing unnecessary processing time in defect-free areas, resolving the time-precision trade-off.