Phased Defect Image Inspection for Variable-Quality Product Images

The patent transitions from a rule-based system to a deep learning-based system that automatically learns patterns from images. The model adapts to variations in image quality caused by equipment aging, lighting changes, and weather conditions by extracting features automatically rather than relying on fixed rules, thereby maintaining detection accuracy across different operating conditions.

The deep learning model automatically learns from training data and adapts to different imaging conditions without requiring manual updates to detection rules. The system self-adjusts to handle variations in image quality through its learned representations, eliminating the need for continuous manual intervention to update detection criteria.

The deep learning model serves as a universal detection system that can handle multiple product types and defect patterns through a single trained model. Instead of requiring separate rules for each product type, the model learns generalizable features from training data and applies them across different products, maintaining consistent performance without increasing operational complexity.

The system performs preliminary training on diverse product images and defect patterns before actual detection. This pre-training phase allows the model to learn and internalize detection criteria for various product types in advance, enabling it to handle new products without requiring manual rule addition during operation.