Multi-Stage Object Detection Using FPGA-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 refines these characteristics, leveraging hardware components like FPGAs for parallel processing and software-driven approaches for enhanced accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single stage classification system is used for object detection, then the device complexity is reduced, but the measurement precision and reliability of object detection deteriorate
Solution Approach 1:
The patent divides the object detection process into multiple stages, where each stage uses a specialized machine-learned model trained for specific characteristics. The first stage model detects basic object presence and location, while the second stage model refines detection and identifies additional characteristics. This segmentation allows each model to specialize in specific tasks, improving overall detection accuracy without requiring a single overly complex model.
Solution Approach 2:
The patent extends the detection process from a single-dimensional approach to a multi-dimensional framework by processing sensor data through multiple sequential stages. Each stage adds a new dimension of analysis, with the first stage providing coarse detection and the second stage providing refined detection and additional characteristics. This dimensional extension improves measurement precision while managing system complexity through modular architecture.
2Measurement precision
If multiple stage classification is implemented, then the measurement precision of object detection is improved, but the device complexity increases
Solution Approach 1:
The patent segments the detection system into multiple independent machine-learned models, each trained on specific characteristics and processing specific aspects of sensor data. This segmentation allows for specialized processing at each stage, improving accuracy while enabling modular deployment and management of system complexity.
Solution Approach 2:
The first stage model performs preliminary detection of object presence and basic characteristics before the second stage model conducts refined detection. This preliminary action filters and prepares data for subsequent processing, improving overall efficiency and allowing the second stage to focus computational resources on refinement rather than initial detection.
3Reliability
If comprehensive object characteristics are detected, then the reliability of autonomous vehicle operation is improved, but the loss of time in processing increases
Solution Approach 1:
The patent segments comprehensive object analysis into multiple stages, with the first stage detecting critical safety-related characteristics quickly and the second stage refining detection and identifying additional characteristics. This segmentation enables time-critical detections to be processed immediately while less time-sensitive characteristics are analyzed subsequently.
Solution Approach 2:
The first stage model performs preliminary detection of essential object characteristics that are critical for immediate safety decisions. By identifying and processing these critical characteristics first, the system ensures reliable operation for time-sensitive decisions while allowing the second stage to complete comprehensive analysis without delaying critical responses.
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.