Multi-Stage Object Detection for Accurate 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 reliability of object detection are insufficient
Solution Approach 1:
The classification process is divided into multiple stages, where each stage uses a different machine-learned model to analyze object data. The first stage performs initial classification with a first model, and the second stage performs refined classification with a second model, thereby improving overall detection accuracy while managing system complexity through structured segmentation
2Reliability
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 machine-learned model performs preliminary classification of object data in the first stage, identifying obvious objects and filtering out non-objects before the second model processes the data in the second stage. This preliminary action reduces the computational burden on the second model and accelerates the overall processing time while maintaining high reliability
3Measurement precision
If more comprehensive object characteristics are analyzed, then the detection accuracy improves, but the use of energy and computational power increases
Solution Approach 1:
The system applies partial analysis in the first stage by using the first machine-learned model to identify only the most critical object characteristics needed for initial classification. The second stage then applies additional analysis only to objects that require further differentiation, avoiding unnecessary computational energy consumption on already clearly identified objects while maintaining high detection accuracy
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.