Operational Domain Labeling for Sensor Frame Training Selection
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Solution Overview
Problem
Existing methods for generating training data for machine learning models using real-world data are inefficient and lack the ability to accurately understand context, relationships, and nuances, leading to incomplete understanding of real-world scenes and limited scalability.
Innovation Solution
A system that identifies and generates training data by comparing sensor data against an annotated dataset to select frames corresponding to specific operational domains, including features and scenarios relevant to the model's purpose, using image processing and selection modules to curate suitable frames for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If real-world data is used directly as training data, then the training data quantity is large, but the data lacks semantic information and contextual understanding
Solution Approach 1:
The patent introduces an annotated dataset as an intermediary between raw sensor data and training data. This annotated dataset contains pre-identified features and semantic labels that bridge the gap between raw visual data and meaningful training information, enabling the system to extract semantically rich training examples without manual annotation of all data.
Solution Approach 2:
The system performs preliminary comparison and feature identification by matching sensor data against the annotated dataset before generating training data. This preliminary action pre-processes the raw data to identify frames containing relevant features, ensuring that only semantically meaningful data is selected for training while maintaining efficiency.
2Reliability
If all sensor data is processed to identify features, then comprehensive feature detection is achieved, but processing time and computational resources increase
Solution Approach 1:
The patent extracts only the necessary subset of data by comparing sensor frames against the annotated dataset to identify frames containing relevant features. Instead of processing all sensor data, the system extracts only those frames that match operational domains of interest, significantly reducing processing time while maintaining detection accuracy.
Solution Approach 2:
The system performs partial processing by selectively analyzing only those frames that contain features matching the operational domains defined in the annotated dataset. This partial action approach avoids the excessive processing of all frames while ensuring that all relevant features are captured for training data generation.
3Adaptability or versatility
If training data is generated without operational domain selection, then all possible features are covered, but the training efficiency and model performance for specific applications decrease
Solution Approach 1:
The patent applies local quality by selecting training data specific to particular operational domains rather than using uniform data for all possible features. The system identifies and selects frames that contain features relevant to specific operational contexts (e.g., pedestrian detection for autonomous vehicles), creating specialized training data that optimizes model performance for targeted applications.
Solution Approach 2:
The system segments the training data generation process by dividing operational domains into distinct categories and selecting frames based on specific domain requirements. This segmentation allows the system to generate specialized training datasets for different applications (e.g., separate datasets for pedestrian detection, vehicle detection, traffic sign recognition) rather than creating a single generic dataset.
Data Source
AI summary
The present disclosure relates to obtaining a data recording. The data recording may correspond to sensor data that includes frame data corresponding to one or more frames that depict a scene as represented by the frame data. The frame data of the one or more frames may be compared against an annotated dataset that may include known features and annotations corresponding to the known features. One or more features in the one or more frames may be identified based at least on the comparison between the frame data and the annotated dataset. A subset of the one or more frames including one or more features associated with one or more operational domains may be determined. Additionally, the subset of frames may be provided to a detection model as training data.