Multi-Stage Object Detection Using Hardware-Accelerated Classification
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately determining the state of objects in their environment, which is crucial for safe operation, due to limitations in existing detection and recognition systems.
Innovation Solution
A computer-implemented method using multiple stage classification, where a first machine-learned model determines initial characteristics of sensor data, and a second model further identifies objects with higher confidence, leveraging hardware components like FPGAs for rapid processing and software-driven approaches for deeper analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single-stage classification system is used for object detection, then the device complexity is lower, but the measurement precision and reliability of object detection deteriorate
Solution Approach 1:
The patent divides the object detection process into multiple stages: a first classification stage that performs initial filtering and a second classification stage that performs detailed analysis. This segmentation allows each stage to specialize in specific tasks, improving overall detection accuracy while managing computational complexity through hierarchical processing.
Solution Approach 2:
The patent introduces a temporal dimension by processing detections across multiple frames. The system accumulates detection results over time and uses temporal consistency to improve measurement precision, transforming a single-frame detection problem into a multi-frame analysis that leverages time as an additional dimension for verification.
2Measurement precision
If multiple stage classification is implemented, then the measurement precision of object detection improves, but the loss of time increases
Solution Approach 1:
The first classification stage performs preliminary filtering of sensor data before the second stage processes the results. By pre-processing and filtering candidates in advance, the system reduces the computational burden on the second stage, thereby reducing overall processing time while maintaining high detection accuracy through sequential specialized processing.
Solution Approach 2:
The system applies classification only to regions of interest identified in the first stage rather than processing the entire sensor data set in both stages. This partial action approach reduces unnecessary computations in the second stage, minimizing time loss while maintaining precision for critical detections.
3Speed
If hardware components like FPGAs are used for rapid processing, then the speed of detection improves, but the device complexity increases
Solution Approach 1:
The patent replaces traditional software-based classification with hardware-accelerated processing using FPGAs for the first classification stage. This substitution of mechanical/computational systems with dedicated hardware circuitry achieves rapid processing speeds while offloading intensive computations from general-purpose processors, managing overall system complexity through specialized hardware modules.
Data Source
AI summary
Systems, methods, tangible non-transitory computer-readable media, and devices for autonomous vehicle operation are provided. For example, a computing system can receive object data that includes portions of sensor data. The computing system can determine, in a first stage of a multiple stage classification using hardware components, one or more first stage characteristics of the portions of sensor data based on a first machine-learned model. In a second stage of the multiple stage classification, the computing system can determine second stage characteristics of the portions of sensor data based on a second machine-learned model. The computing system can generate an object output based on the first stage characteristics and the second stage characteristics. The object output can include indications associated with detection of objects in the portions of sensor data.