Vision Inspection Classifier With Human-in-the-Loop Retraining

The patent transforms the inspection system from rule-based classification to deep learning-based classification. The neural network models dynamically adjust classification parameters based on learned patterns from training data, replacing static human-defined rules with adaptive algorithms that can distinguish between genuine defects and normal variations, thereby reducing false positives while maintaining comprehensive inspection coverage

The patent replaces the mechanical rule-based inspection system with an intelligent deep learning system. Traditional AOI used explicit classification rules that manually defined acceptable variations, while the new system uses neural networks that automatically learn patterns from data, substituting rigid mechanical logic with flexible computational intelligence that adapts to complex manufacturing variations

The patent implements a self-service inspection system where the deep learning models autonomously perform classification and defect detection without requiring manual intervention for each board. The system automatically learns from training data, makes independent classification decisions, and only flags uncertain cases for human review, enabling thorough defect detection while significantly improving inspection throughput by eliminating redundant manual checking

The patent incorporates feedback mechanisms where inspection results are continuously fed back into the system to refine and retrain the neural network models. This feedback loop allows the system to learn from actual defect patterns and improve its classification accuracy over time, enabling more accurate automated inspection that reduces the need for manual review while maintaining high defect detection accuracy

The patent uses copying by creating synthetic training data that replicates real manufacturing variations. Instead of manually tuning rules for each variation, the system generates artificial images and defect patterns that mimic real-world conditions, allowing the neural network to learn from these copies and generalize to actual production environments, thereby improving classification accuracy without proportionally increasing system complexity

The patent performs preliminary training of neural network models on extensive datasets before deployment to production lines. The models are pre-trained to recognize defect patterns and normal variations in advance, so that during actual inspection operations, the system can make accurate classifications without requiring complex real-time rule tuning or adjustment, reducing operational system complexity while maintaining high accuracy

The patent implements a dynamic inspection system where the neural network models can be quickly retrained or fine-tuned for new product types. The system adapts its classification parameters dynamically based on the specific product being inspected, allowing high reliability for new products without requiring manual reconfiguration of inspection rules. The model can learn new defect patterns and variations specific to each product type through rapid retraining on available training data

The patent creates a universal inspection system where a single neural network architecture can be applied across multiple product types through transfer learning. The base model learns general defect detection capabilities and can be fine-tuned for specific products using relatively small training datasets, enabling the system to maintain high reliability for diverse product types without requiring separate specialized inspection systems for each product category