Imaging Device Readout Unit Segmentation for Real-Time Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image recognition technologies face challenges in achieving real-time presentation of determination bases for moving images, particularly in applications like automated driving, due to limitations in speeding up the calculation of determination bases for improving image quality and processing loads.
Innovation Solution
An imaging device and method that perform image recognition and determination basis calculation for each readout unit within a pixel region, utilizing a trained machine learning model and neural networks to process pixel signals, allowing for high-speed recognition and real-time presentation of determination bases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image recognition is performed for the entire pixel region to improve recognition accuracy, then recognition precision is improved, but processing time increases and real-time presentation becomes difficult
Solution Approach 1:
The pixel region is divided into multiple readout units, and recognition processing is performed independently for each readout unit instead of the entire region. This segmentation allows parallel processing and reduces the time required for recognition while maintaining accuracy through comprehensive coverage of all regions.
Solution Approach 2:
The patent introduces a spatial dimension to the recognition process by performing recognition at multiple locations (readout units) simultaneously across the pixel region. This multi-location parallel processing approach transforms the single sequential recognition process into a multi-dimensional parallel system, reducing overall processing time.
2Measurement precision
If image quality is improved by increasing pixel density to enhance recognition accuracy, then recognition precision is improved, but processing load increases
Solution Approach 1:
By dividing the high-resolution pixel region into multiple readout units, the patent reduces the processing load for each individual recognition task. Each readout unit handles a smaller subset of pixels, making the processing more manageable and less computationally intensive while collectively covering the entire high-resolution region.
Solution Approach 2:
The patent performs recognition processing for each readout unit independently and in parallel, which can be seen as performing partial recognition actions simultaneously across multiple regions. This approach distributes the processing load across multiple independent operations rather than requiring one complex sequential operation on the entire high-resolution image.
3Measurement precision
If determination basis calculation is performed for the entire image to improve explanation accuracy, then determination basis precision is improved, but calculation speed decreases
Solution Approach 1:
The determination basis calculation is segmented and performed for each readout unit independently. This allows the calculation to be distributed across multiple parallel processing streams, significantly improving calculation speed while maintaining precision by comprehensively analyzing all regions. The segmented approach enables real-time presentation of determination bases.
4Reliability
If recognition processing is performed for the entire pixel region to maintain comprehensive analysis, then recognition completeness is improved, but power consumption increases
Solution Approach 1:
By segmenting the pixel region into multiple readout units and processing them in parallel, the patent reduces the time required for complete recognition analysis. This time reduction directly lowers power consumption since the system can return to idle or low-power states more quickly, while still maintaining recognition completeness by covering all regions.
Solution Approach 2:
The patent enables continuous recognition processing across multiple readout units without interruption, allowing the system to efficiently process the entire pixel region in a streamlined manner. This continuous parallel processing reduces idle time and improves overall energy efficiency compared to sequential processing approaches.
Data Source
AI summary
An imaging device includes: an imaging section that has a pixel region where a plurality of pixels is arrayed, a readout unit control section that controls readout units each set as a part of the pixel region, a readout control section that controls readout of pixel signals from the pixels included in the pixel region for each of the readout units set by the readout unit control section, a recognition section that has a machine learning model trained on the basis of leaning data, and a determination basis calculation section that calculates a determination basis of a recognition process performed by the recognition section. The recognition section performs the recognition process for each of the readout units. The determination basis calculation section calculates a determination basis for each of the readout units.