Sensor-Specific Image Recognition Using Adaptive Feature Masks
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Solution Overview
Problem
Existing image recognition systems face challenges in accurately classifying input patterns and generalizing to unseen data, particularly when using different types of image sensors with varying optical characteristics.
Innovation Solution
A neural network-based image recognition method that employs a feature extraction layer to extract features from input images, applying both a fixed and a variable mask to enhance recognition accuracy by emphasizing regions of interest specific to the image sensor's optical characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a generic image recognition model is used, then the system can process images from any sensor, but recognition accuracy decreases for specific sensor types
Solution Approach 1:
The recognition model is segmented into multiple independent branches, each dedicated to a specific image sensor type. Each branch contains sensor-specific layers trained on data from that particular sensor, allowing the system to maintain high recognition accuracy for each sensor type while still providing universal coverage across multiple sensors through the modular architecture.
2Measurement precision
If sensor-specific training data is used, then recognition accuracy for that sensor improves, but the system cannot generalize to other sensor types
Solution Approach 1:
The system employs a universal multi-branch architecture where each branch is specialized for a specific sensor type but the overall system can handle multiple sensor types simultaneously. The shared lower layers extract generic features that are useful across all sensor types, while the sensor-specific upper layers adapt these features to each sensor's characteristics, achieving both specialization and universality.
3Measurement precision
If multiple sensor-specific models are maintained, then recognition accuracy for each sensor is maximized, but device complexity increases
Solution Approach 1:
Multiple sensor-specific recognition models are merged into a single unified multi-branch network. The branches share common lower-level feature extraction layers and can be integrated through a common output layer, reducing the overall complexity compared to maintaining completely separate models while still preserving sensor-specific optimization through dedicated processing paths.
4Adaptability or versatility
If the model is trained on diverse sensor data, then adaptability to different sensors improves, but recognition precision for individual sensors decreases
Solution Approach 1:
The model implements local quality by having uniform lower layers that process generic features from all sensors equally, followed by sensor-specific upper layers that apply specialized processing tailored to each sensor type's characteristics. This allows the model to maintain adaptability across sensors while achieving high precision for each individual sensor type through localized specialization.
Data Source
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AI summary
A method with image recognition includes: extracting, using a feature extraction layer, feature data from an input image received by an image sensor; and outputting a recognition result of an object appearing in the input image, by applying a fixed mask and a variable mask to the extracted feature data, wherein the variable mask is adjusted in response to the extracted feature data.