Adaptive Sensor 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 resources, necessitating efficient data processing and resolution management.
Innovation Solution
A machine learning model is trained to determine optimal data levels for processing regions of sensor data, allowing for variable resolutions and compression levels, enabling efficient resource utilization by processing critical areas at higher resolution while reducing processing time and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is processed at high resolution to maintain accuracy, then measurement precision is improved, but processing time increases and resource consumption increases
Solution Approach 1:
The sensor data is divided into multiple regions of interest, each processed at different resolution levels. Critical regions requiring high detection accuracy are processed at high resolution, while non-critical regions are processed at lower resolution, thereby reducing overall processing time while maintaining necessary detection accuracy.
Solution Approach 2:
Different regions of the sensor data are assigned different quality levels (resolution levels) based on their importance. High-resolution processing is applied locally to critical regions where detection accuracy is paramount, while lower resolution is applied to less critical regions, optimizing the trade-off between accuracy and processing speed.
2Measurement precision
If sensor data is processed at high resolution to maintain accuracy, then measurement precision is improved, but resource consumption increases
Solution Approach 1:
The data processing workload is segmented into different resolution levels based on regional importance. By processing only critical regions at high resolution and non-critical regions at lower resolution, the overall computational resources and energy consumption are reduced while maintaining detection accuracy where it matters most.
Solution Approach 2:
The system applies local quality variation by assigning different resolution levels to different spatial regions of the sensor data. This ensures that high processing quality (and associated resource consumption) is concentrated only where necessary for accurate detection, rather than uniformly applied across all data.
3Measurement precision
If data is processed at high resolution, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The processing pipeline is segmented to handle different regions at different resolution levels simultaneously. This allows the system to maintain high processing throughput by parallelizing work across multiple resolution levels while ensuring that critical regions receive the high-resolution processing necessary for accurate detection.
Solution Approach 2:
The system dynamically adjusts the resolution level applied to different regions based on their importance and the current processing context. This dynamic adaptation allows the system to optimize the balance between processing accuracy and throughput, allocating high-resolution processing only when and where it is most beneficial for detection accuracy.
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.