Deep Learning Defect Inspection With In-Line Model Fine-Tuning
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Solution Overview
Problem
Existing product inspection systems using rule-based algorithms suffer from low judgment accuracy, frequent false defects, and difficulty in handling situational changes, leading to reduced product production ratios and increased costs.
Innovation Solution
A product inspection method and system that links a predeveloped deep learning-based classification model to the existing inspection system, allowing for fine-tuning of the model to correct errors instantly, thereby improving inspection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based algorithms are used for product inspection, then the system is simple to implement, but the judgment accuracy is low and false defects occur frequently
Solution Approach 1:
The patent transitions from rule-based algorithms to deep learning-based classification models, fundamentally changing the inspection parameter from simple rule evaluation to complex pattern recognition. This allows the system to learn optimal patterns for defect discrimination from large datasets, significantly improving judgment accuracy while maintaining system operability through automated model training and fine-tuning capabilities
2Device complexity
If rule-based algorithms are used for product inspection, then the system structure is simple, but the system cannot cope with situational changes such as minute differences in position and lighting
Solution Approach 1:
The patent implements dynamic adaptability by enabling the deep learning model to continuously learn from new data and adjust to situational changes. The system can handle minute differences in position, lighting, and other environmental factors through the model's learned representations, while providing mechanisms for ongoing fine-tuning to maintain performance under varying conditions
Solution Approach 2:
The system transforms from static rule-based parameters to dynamic learned parameters that automatically adapt to situational changes. The deep learning model learns robust feature representations that are invariant to minor variations in position, lighting, and other environmental factors, enabling the system to maintain high accuracy across diverse inspection conditions
3Measurement precision
If deep learning-based technology is applied to product inspection, then the judgment accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent segments the deep learning system into modular components: a pre-trained classification model for defect detection, a fine-tuning module for continuous improvement, and an integration layer with the existing inspection system. This modular architecture manages complexity by allowing each component to be developed, trained, and maintained independently while working together to achieve high detection accuracy
Solution Approach 2:
The system employs pre-trained deep learning models that have already learned general defect patterns from large datasets before deployment. This preliminary training action reduces the complexity of on-site configuration and enables the system to achieve high accuracy from the start, with subsequent fine-tuning further adapting the model to specific production conditions without requiring complete system reconfiguration
4Reliability
If deep learning-based technology is applied to product inspection, then the robustness against disturbances is improved, but the difficulty of system integration with existing systems increases
Solution Approach 1:
The patent introduces an intermediary integration layer that bridges the deep learning model and the existing inspection system. This intermediary handles data format conversions, coordinate system transformations, and result integration, thereby maintaining the robustness of the deep learning model against disturbances while simplifying the integration process with legacy systems through standardized interfaces and protocols
Data Source
AI summary
The present disclosure in some embodiments provides a product inspection method and a system based on deep learning for detecting a product defect. The present disclosure provides a product inspection method and system for detecting product defects by linking a predeveloped deep learning-based classification model to interwork with the existing product inspection system while fine-tuning the classification model to be maintained or supplemented by instantly correcting errors of the classification model, thereby improving the accuracy of product quality inspection.