Mobile Component Identification Using 3D CAD Training Images
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Solution Overview
Problem
Existing component identification methods are not user-friendly and reliable, particularly in visual contexts, lacking efficient training data for machine learning models to accurately identify components across diverse orientations, backgrounds, and textures.
Innovation Solution
A method utilizing 3D model files from CAD data to generate diverse training images for machine learning models, including varying orientations, backgrounds, and textures, with duplicate detection to enhance model accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained using traditional photograph datasets, then the models can identify components, but the training data lacks diversity in orientations, backgrounds, and textures leading to poor identification accuracy
Solution Approach 1:
The patent creates synthetic training images by rendering 3D models of components from CAD data. These synthetic images replicate real-world variations in orientation, background, and texture without requiring actual photographs of components in all possible configurations. The system generates numerous synthetic images representing the same component under different conditions, effectively copying the diversity needed for training without capturing each variant physically.
Solution Approach 2:
The system varies multiple parameters when generating synthetic training images including orientation angles, background types, lighting conditions, and texture variations. By systematically changing these parameters across the training dataset, the model learns to recognize components regardless of their specific orientation or background context, directly addressing the adaptability problem.
2Measurement precision
If comprehensive training data covering all possible orientations and backgrounds is collected, then model accuracy improves, but data collection cost and computational resources increase significantly
Solution Approach 1:
The patent replaces the mechanical process of physically capturing photographs of components in various orientations and backgrounds with a computational rendering system. Instead of using cameras to capture real-world images requiring physical manipulation of components, the system uses computer graphics to generate synthetic images from 3D CAD models, dramatically reducing computational and physical resources needed.
Solution Approach 2:
The system performs preliminary generation of comprehensive training data through synthetic rendering before actual component identification tasks. By pre-generating a large diverse dataset of synthetic images with known ground truth labels, the model is trained in advance on varied conditions, eliminating the need for costly post-collection processing or extensive real-world data gathering.
3Adaptability or versatility
If synthetic images are generated from 3D models, then training data diversity is improved, but the complexity of generating and processing these images increases
Solution Approach 1:
The patent segments the image generation process into distinct modular components: (1) loading and parsing CAD model data, (2) generating 3D renderings with varied parameters, (3) creating synthetic training datasets, and (4) training the machine learning model. This segmentation allows each component to be optimized independently and facilitates easier implementation and maintenance of the overall system.
Solution Approach 2:
The synthetic image generation system serves multiple functions simultaneously: it creates training data for object detection, generates data for segmentation tasks, provides varied orientations for robust recognition, and creates diverse background contexts. This multi-functionality reduces the need for separate specialized datasets and processing pipelines, simplifying the overall system architecture.
4Reliability
If the machine learning model is trained to detect components accurately, then identification reliability improves, but the time required for training and processing increases
Solution Approach 1:
By using synthetic images that copy the essential visual characteristics of real components across diverse conditions, the system creates a training dataset that is both comprehensive and efficiently processable. The synthetic images preserve the critical features needed for reliable identification while avoiding the variability and processing overhead of real photographs, enabling faster training without sacrificing reliability.
Data Source
AI summary
A method includes obtaining a test image captured by a camera of a mobile device, the image including a test component; processing the test image using a machine learning model, wherein the machine learning model has been trained by obtaining a 3D model file representing a 3D model of a training component, generating a plurality of images of the training component based on the 3D model file, the plurality of images differing from one another in at least one of orientation, background, or texture, determining locations of the training component, and training the machine learning model using the plurality of images and the locations of the training component, wherein the machine learning model is trained to detect the training component in photographs captured by mobile devices; and presenting one or more candidate identities of the test component based on an output of the machine learning model.