Semantic Segmentation of Low-Contrast Materials Using ViT Mask Fusion
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Solution Overview
Problem
Existing computer vision techniques for semantic segmentation produce inconsistent and inaccurate results, particularly with porous, amorphous, and low-contrast materials like carbon black-binder (CBD) in electrodes, and often require large amounts of annotated training data.
Innovation Solution
Employ deep learning techniques using transformer models, such as Segment Anything Model (SAM) and U-Net, to adapt to specific materials in images, integrating multiple segmentation masks through a neural network combiner model, allowing for accurate semantic segmentation with optional user prompting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computer vision techniques are used for semantic segmentation, then the method is simple and easy to implement, but the segmentation accuracy and consistency deteriorate significantly for porous, amorphous, and low-contrast materials
Solution Approach 1:
The patent divides the segmentation task into multiple specialized transformer models, each trained to segment a specific material type (e.g., graphite, silicon, CBD, pores). This segmentation of the task allows each model to specialize in detecting specific material characteristics, improving overall accuracy for complex, low-contrast materials while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent employs transformer models that can be adapted to segment multiple different materials within the same electrode structure. The combiner model integrates outputs from multiple specialized transformers, creating a universal system that can handle diverse material types (conductive polymers, metal oxides, carbon materials) without requiring separate dedicated models for each material class.
2Measurement precision
If existing semantic segmentation methods are used, then the approach is straightforward, but it requires large amounts of annotated training data to achieve acceptable performance
Solution Approach 1:
The patent changes the fundamental parameters of the model architecture by using transformer models with attention mechanisms instead of conventional convolutional networks. This parameter change allows the model to achieve high segmentation accuracy with significantly reduced training data requirements, as transformers can learn complex material characteristics more efficiently from fewer examples through their self-attention mechanisms.
Solution Approach 2:
The patent utilizes pre-trained transformer models (such as Segment Anything Model) that have been trained on large general datasets, then adapts them to specific electrode materials through fine-tuning. This copying approach allows the model to leverage pre-learned visual patterns and structures, reducing the need for extensive domain-specific annotated data while maintaining high segmentation accuracy for porous and low-contrast materials.
3Difficulty of detecting and measuring
If conventional segmentation models are applied to complex materials, then the model structure is simple, but the ability to detect and measure porous and low-contrast materials deteriorates
Solution Approach 1:
The patent segments the detection task by creating separate transformer models for each material type, allowing each model to be optimized for detecting specific material characteristics. For porous and low-contrast materials, this specialized approach enables the model to focus on subtle textural and contrast patterns that would be missed by a single general-purpose model.
Solution Approach 2:
The combiner model acts as an intermediary that integrates outputs from multiple specialized transformers. This intermediary layer reconciles the predictions from different material-specific models, resolving conflicts and synthesizing a final segmentation map that accurately represents complex material structures, particularly improving detection of porous and low-contrast regions.
Data Source
AI summary
Embodiments determine semantic segmentation of real-world objects. One such embodiment obtains an image of a real-world object. The obtained image is processed using a plurality of vision transformer (ViT) models to generate a plurality of segmentation masks. Each ViT model is configured to output a respective segmentation mask corresponding to a respective target material. Using a neural network combiner model, a multiphase semantic segmentation mask is generated based on the plurality of segmentation masks. The neural network combiner model is trained to integrate outputs of the plurality of ViT models. Semantic segmentation of the real-world object is thereby determined.