Multi-Stage Object Detection for Autonomous Vehicle Recognition
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Solution Overview
Problem
Existing autonomous vehicle systems face challenges in accurately determining the state of objects in their environment, which is crucial for safe operation.
Innovation Solution
A computer-implemented method using multiple stage classification, where a computing system processes object data from sensors to determine first and second stage characteristics using machine-learned models, generating an object output that indicates detected objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single stage classification is used for object detection, then the device complexity is low, but the measurement precision and detection accuracy are insufficient
Solution Approach 1:
The classification system is divided into multiple stages (first stage, second stage, etc.), where each stage performs partial classification tasks. The first stage processes all sensor data to identify potential objects, and subsequent stages refine the classification of detected objects, thereby improving overall detection accuracy while managing system complexity through modular design.
Solution Approach 2:
The system transitions from a single-dimension classification approach to a multi-dimensional staged classification approach. Each stage adds another dimension of analysis, processing different aspects or levels of object characteristics sequentially, which enhances detection precision by examining objects from multiple analytical perspectives.
2Measurement precision
If multiple stage classification is implemented, then the object detection accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The first stage performs preliminary classification on all sensor data to identify potential objects of interest. By pre-processing and filtering data in the first stage, the system reduces the volume of data requiring intensive processing in subsequent stages, thereby minimizing overall processing time while maintaining high detection accuracy.
Solution Approach 2:
Each subsequent stage extracts and focuses only on the specific characteristics or subsets of objects detected in previous stages. The second stage, for example, processes only the objects identified by the first stage, extracting detailed features for refined classification. This selective processing reduces computational overhead and processing time compared to analyzing all data at full detail from the start.
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.