Solid-State Imaging Recognition With Feedback-Based Adaptation
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Solution Overview
Problem
Existing solid-state imaging devices face challenges in improving the accuracy of neural network processing due to environmental changes and variations, leading to decreased recognition rates without effective optimization and retraining mechanisms.
Innovation Solution
A solid-state imaging device with integrated control units for optimizing exposure time, image processing, and neural network model retraining, along with selective data output and compression, enables improved recognition accuracy by adjusting parameters and conditions within the device or through external server assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural network processing is performed in signal processing circuits to improve latency and privacy, then recognition rate decreases due to environmental changes and variations
Solution Approach 1:
The control unit performs preliminary optimization of image acquisition parameters and preliminary retraining of the neural network model before recognition processing. This includes optimizing exposure time, gain, and other imaging parameters in advance, and retraining the model with updated data to prepare for varying environmental conditions, thereby maintaining high recognition rates across different environments.
Solution Approach 2:
The system implements feedback mechanisms where the control unit monitors recognition results and environmental conditions, then uses this feedback to continuously optimize image acquisition parameters and retrain the neural network model. This closed-loop feedback ensures the system adapts to environmental changes while maintaining reliable recognition performance.
2Reliability
If optimization and retraining are performed externally to improve recognition accuracy, then data transmission requirements increase, affecting bandwidth and privacy
Solution Approach 1:
The patent extracts only the essential optimization parameters and model updates that need to be transmitted externally, while performing the bulk of optimization and retraining operations locally within the imaging device. This selective extraction minimizes data transmission requirements while still achieving improved recognition accuracy through targeted external assistance when needed.
Solution Approach 2:
The imaging device performs self-optimization of image acquisition parameters and self-retraining of the neural network model through its integrated control unit. This self-service capability reduces dependence on external systems, minimizing data transmission requirements while maintaining high recognition accuracy through autonomous adaptation to environmental conditions.
3Productivity
If multiple chips are connected into packages to perform signal processing, then device complexity increases
Solution Approach 1:
The patent combines the pixel array, converter, image processing unit, digital signal processing unit, and control unit into an integrated imaging device structure. This merging of previously separate components into a unified system reduces device complexity while maintaining comprehensive signal processing capabilities, as the components work together synergistically within a single integrated architecture.
Solution Approach 2:
The control unit performs multiple functions including optimizing image acquisition parameters, controlling the pixel array and converter, managing the image processing unit, and supervising neural network model retraining. This multi-functional design consolidates what would otherwise require separate dedicated components, reducing overall device complexity while maintaining full signal processing capability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances recognition accuracy and reduces system costs by optimizing image acquisition and neural network models within the device, while ensuring privacy and security by minimizing data transmission, and allows for efficient bandwidth usage.
Implementation Method 1
a pixel array having a plurality of pixels performing photoelectric conversion
Data Source
AI summary
To improve the accuracy of the recognition processing used in an image sensor. A solid-state imaging device includes a pixel array, a converter, an image processing unit, a digital signal processing unit, and a control unit. The pixel array has a plurality of pixels that perform photoelectric conversion. The converter converts an analog pixel signal output from the pixel array into digital image data. The image processing unit performs image processing on the digital image data. The digital signal processing unit performs recognition processing on the digital image data output by the image processing unit. The control unit performs optimization regarding at least one acquisition processing operation among acquisition of the analog pixel signal, acquisition of the digital image data, and acquisition of a result of the recognition processing based on the result of the recognition processing.