Synthetic Defect Image Generation for Diverse AI Training Data
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Solution Overview
Problem
Training AI models to identify defects in objects is challenging due to the high cost and impracticality of capturing a diverse range of images under varying environmental conditions, leading to inefficiencies and reduced accuracy.
Innovation Solution
A synthetic defect generation system that creates realistic images of defects under different lighting and environmental conditions using 3D models, enabling the generation of a broad spectrum of training data for defect detection models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If real images of defects under various environmental conditions are captured for training, then the model training data diversity is improved, but the cost and time consumption increase substantially
Solution Approach 1:
The patent uses 3D models to create synthetic copies of defects instead of capturing real images. The system generates training images by rendering defects on 3D object models under various virtual environmental conditions, eliminating the need for physical photography while maintaining data diversity for model training
Solution Approach 2:
The patent introduces a computer-generated intermediary layer between the physical object and the training data. Instead of directly photographing real defects, the system uses 3D modeling and rendering as an intermediary process to generate synthetic training images that capture the essential defect characteristics without requiring physical capture under various conditions
2Adaptability or versatility
If real images of defects under various environmental conditions are captured for training, then the model training data diversity is improved, but the cost increases substantially
Solution Approach 1:
The patent uses 3D models to create synthetic copies of defects instead of capturing real images. The system generates training images by rendering defects on 3D object models under various virtual environmental conditions, eliminating the need for physical photography while maintaining data diversity for model training
Solution Approach 2:
The patent replaces expensive physical defect capture processes with inexpensive computational rendering. The synthetic training images are generated through software-based 3D rendering, which has minimal marginal cost compared to the expensive equipment, labor, and logistics required for capturing real defect images under various conditions
3Device complexity
If manual defect inspection is used, then the system complexity is reduced, but the labor cost and detection time increase
Solution Approach 1:
The patent performs preliminary actions by pre-training AI models with extensive synthetic defect data before deployment. The system prepares comprehensive training datasets using 3D rendering under various conditions in advance, enabling the model to be pre-adapted to diverse defect scenarios, which reduces detection time during actual operation
Solution Approach 2:
The patent replaces manual mechanical inspection processes with automated AI-based detection. The system substitutes human inspectors with trained neural networks that can rapidly analyze images, eliminating the time-consuming nature of manual inspection while reducing overall system operational complexity
Data Source
AI summary
Approaches presented herein provide for the generation of images representing physical objects having one or more synthetic but realistic defects or other such variations or augmentations. A generative system can use characteristics of a defect and an environment to create three-dimensional (3D) models of both the environment and the defect, which can be used to generate one or more images of the defect, or combinations of defects, in various environments that may have different lighting conditions. A system can generate random, semi-random, or specifically-instructed variations of the synthetic environment and defect to simulate different visualizations of the defect under varied environmental conditions. A system can further emulate different presentations of defects by adding or combining various defect types, as well as simulating different defect severity levels. Through the integration of these synthetic defects, a synthetic defect generation system can generate a diverse array of synthetic images, such as can be used to train defect detection models.