Region-Based Data Resolution for Real-Time Object Detection
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Solution Overview
Problem
Resource-constrained environments, such as autonomous vehicles, face challenges in accurately and timely processing sensor data due to limited processing power and memory, which affects object detection, classification, and other computational tasks.
Innovation Solution
A machine learning model is trained to determine optimal data levels for processing regions of sensor data, allowing for efficient resource allocation by identifying areas that benefit from higher or lower resolutions, thereby optimizing processing and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is processed at high resolution to maintain accuracy, then object detection and classification accuracy is improved, but processing time and memory requirements increase
Solution Approach 1:
The patent segments sensor data into multiple regions of interest based on object detection confidence levels and spatial importance. High-resolution processing is applied only to critical regions while other areas use lower resolution, thereby reducing overall processing time while maintaining detection accuracy for important objects
Solution Approach 2:
The patent implements local quality by applying different processing resolutions to different spatial regions of the sensor data. Critical regions with low-confidence detections or high safety importance receive high-resolution processing, while less critical regions use lower resolution, optimizing the balance between accuracy and processing speed
2Measurement precision
If sensor data is processed at high resolution to maintain accuracy, then object detection and classification accuracy is improved, but memory requirements increase
Solution Approach 1:
The patent divides sensor data into segmented regions with different memory allocation priorities. Only regions requiring high-precision processing are loaded into high-resolution memory buffers, while other regions use compressed or lower-resolution representations, significantly reducing total memory requirements while preserving detection accuracy for critical areas
Solution Approach 2:
The patent applies local quality by allocating high memory resources only to specific spatial regions where high-resolution data is necessary for accurate detection. Other regions use reduced memory allocation with lower resolution data, optimizing the trade-off between memory consumption and detection performance
3Measurement precision
If all sensor data is processed uniformly at high resolution, then processing accuracy is improved, but resource efficiency deteriorates
Solution Approach 1:
The patent segments sensor data processing into multiple priority levels and spatial regions, applying high-resolution processing only where necessary for safety-critical detections. This selective approach maintains high processing accuracy for important objects while dramatically improving resource efficiency by avoiding unnecessary high-resolution processing of less critical data
Solution Approach 2:
The patent implements local quality by varying processing resolution based on spatial location and detection confidence. Regions with high safety importance or low-confidence detections receive high-resolution processing, while other regions use lower resolution, optimizing the balance between processing accuracy and resource efficiency across different areas of the sensor data
Data Source
AI summary
Techniques are discussed for determining a data level for portions of data for processing. In some cases, a data level can correspond to a resolution level, a compression level, a bit rate, and the like. In the context of image data, the techniques can determine a region of first image data to be processed a high resolution and a region of second image data to be processed at a low resolution. The regions can be determined by a machine learned algorithm that is trained to output identifications of such regions. Training data may be determined by identifying differences in outputs based on the first and second image data. The image data associated with the determined regions and the determined resolutions can be processed to perform object detection, classification, segmentation, bounding box generation, and the like, thereby conserving processing, bandwidth, and/or memory resources in real time systems.