Intelligent Defect Detection Using ML Image Synthesis
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Solution Overview
Problem
Traditional defect detection systems face challenges in adapting to misalignment and tiny shifts in defect images, require labor-intensive human labeling for training data, and struggle with imbalanced defect image data, leading to inefficiencies in defect detection and cause determination.
Innovation Solution
An intelligent defect detection platform utilizing classic and machine learning-based image augmentation, generative adversarial networks, reinforcement learning, and deep CNN models for real-time defect characterization and cause analysis, enabling automated defect detection, classification, and remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based object detection is used to detect defects, then the system is simple to implement, but it struggles to adapt to misalignment and tiny shifts in defect images
Solution Approach 1:
The patent replaces rule-based detection mechanisms with machine learning models that can automatically adapt to variations in defect images. The ML models learn from training data to identify defects regardless of alignment or position, substituting rigid rule-based systems with flexible data-driven approaches.
Solution Approach 2:
The patent transforms the detection approach by changing from fixed rule-based parameters to learned parameters from training data. The system adjusts detection parameters dynamically based on patterns learned from diverse defect images, enabling adaptation to misalignment and shifts without manual rule adjustments.
2Adaptability or versatility
If machine learning models are used to detect defects, then adaptability improves, but training requires expensive and labor-intensive human labeling
Solution Approach 1:
The patent performs preliminary actions by pre-training models on large datasets of defect images before deployment. This advance preparation allows the models to be ready for production use without requiring real-time human labeling, reducing operational time and effort.
Solution Approach 2:
The patent uses synthetic defect images generated by copying and transforming real defect images to create training datasets. This approach reduces the need for extensive manual labeling by generating sufficient training data through image synthesis techniques.
3Measurement precision
If machine learning models are used to detect defects, then detection accuracy can improve, but imbalanced defect image data negatively affects model accuracy
Solution Approach 1:
The patent applies local quality by focusing training efforts on rare defect types that are underrepresented in the dataset. The system prioritizes learning from minority class examples through techniques like oversampling or targeted data collection, ensuring adequate detection capability for all defect types despite overall data imbalance.
Solution Approach 2:
The patent uses partial or excessive action by collecting and training on more defect images than strictly necessary, particularly for rare defect types. This excessive data collection approach ensures sufficient training examples for all defect categories, improving model reliability across the full range of possible defects.
4Device complexity
If traditional defect detection systems are used, then system complexity is low, but they cannot determine defect causes rapidly in real-time
Solution Approach 1:
The patent segments the defect detection process into distinct automated components: image capture, defect detection, characterization, and cause determination. Each segment is handled by specialized algorithms that work together in a pipeline, enabling real-time processing without requiring complex manual analysis procedures.
Solution Approach 2:
The patent implements self-service by enabling the system to automatically determine defect causes without human intervention. The ML models analyze defect characteristics and ancillary data to autonomously identify root causes, allowing rapid real-time decision-making while keeping the system relatively simple to operate.
Data Source
AI summary
Implementations include receiving image data representative of images of items within a physical environment and depicting defects in at least one item, providing one or more of a set of augmented images using image augmentation based on the image data and a set of synthetic images using ML-based image synthesis based on the image data, processing one of the set of augmented images and the set of synthetic images using an ML model to provide a set of defect characteristics representative of defects in the at least one item, providing one or more root causes of each of the one or more defects by processing the set of defect characteristics and ancillary data, the ancillary data representative of the physical environment, and generating one or more alerts based on the one or more root causes for remediation of at least one root cause of the one or more defects.