Sensor Data Signature Selection for Annotation Efficiency
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Solution Overview
Problem
Machine learning methods for automated perception and scene recognition require large amounts of manually annotated data, leading to high time and cost burdens due to the labor-intensive nature of scene annotation.
Innovation Solution
A computer-implemented method determines candidate data sets for labeling by analyzing sensor data sets to identify suitable data sets for annotation using signatures, which are numeric representations generated through neural networks or machine learning methods, allowing for efficient selection and reduction of data sets that need manual labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If large amounts of sensor data sets are collected for machine learning, then the amount of training data increases, but the time and cost for manual annotation increases
Solution Approach 1:
The system performs preliminary actions by automatically generating signatures for sensor data sets before manual annotation is needed. These signatures pre-process and characterize the data, enabling rapid identification of suitable candidates for annotation without requiring manual inspection of every data set, thus reducing annotation time while maintaining adequate training data quantity
Solution Approach 2:
The signature acts as an intermediary between the raw sensor data sets and the manual annotation process. Instead of annotators directly reviewing large volumes of raw data, the signature serves as a compressed representation that mediates the selection process, allowing efficient filtering and identification of annotation candidates while reducing overall annotation time
2Reliability
If manual annotation is performed on all sensor data sets, then the completeness of labelled data increases, but the cost and time consumption increases
Solution Approach 1:
The system applies local quality by not treating all sensor data sets uniformly for annotation. Instead, it identifies and prioritizes specific local regions or characteristics within the data (through signature analysis) that are most valuable for training, focusing annotation resources on data sets with high annotation value rather than uniformly processing all data
Solution Approach 2:
The system changes the parameter representation of sensor data sets from raw form to signature form. This parameter transformation enables efficient comparison and selection of data sets for annotation, allowing the system to maintain reliability by selecting high-quality data while improving productivity through rapid signature-based filtering and selection
3Measurement precision
If signatures are generated for all sensor data sets, then the ability to identify similar scenes improves, but the computational complexity increases
Solution Approach 1:
The system extracts the essential characteristics from sensor data sets by generating signatures that capture the most important features for scene similarity identification. This extraction process condenses complex raw data into compact signature representations, maintaining measurement precision for similarity identification while reducing the computational complexity of subsequent comparison and selection operations
Data Source
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AI summary
A computer implemented method for determining candidate data sets for labelling comprises the following steps carried out by computer hardware components: determining a plurality of sensor data sets; determining a respective signature for each of the plurality of sensor data sets; determining, based on the signature of the respective sensor data set, for each of the plurality of sensor data sets whether the respective sensor data set is a candidate data set for labelling; and providing the sensor data set to a labeling instance for labelling if the sensor data set is a candidate data set for labelling.