Sensor Data Annotation with Condition-Based Neural Network Retraining
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Solution Overview
Problem
Existing methods for annotating sensor data, particularly from image-capture sensors, are limited by the need for large quantities of diverse training data and manual quality checks, which are time-intensive and costly, making large-scale annotation projects infeasible.
Innovation Solution
A method that groups sensor data frames based on environment conditions at the time of recording, uses a neural network for initial annotation, checks quality, retrains the network if necessary, and iteratively refines the annotation process to achieve high quality without extensive manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If neural networks are used for automated annotation, then productivity increases, but annotation quality deteriorates requiring time-intensive quality checks
Solution Approach 1:
The patent segments the annotation process into distinct phases: initial automated annotation using neural networks, quality checking of samples, identification of problematic environment conditions, targeted retraining, and final annotation. This segmentation allows automated high-throughput processing while maintaining quality through focused human review of specific problematic cases rather than all annotations.
Solution Approach 2:
The patent changes the parameter of neural network training by identifying specific environment conditions where annotation quality deteriorates and retraining the network specifically for those conditions. This targeted parameter adjustment improves annotation quality for problematic cases without requiring complete reannotation, thus maintaining productivity while improving precision.
2Manufacturing precision
If manual quality checks are applied to all annotations, then annotation quality is maintained, but productivity decreases due to linear relationship between project volume and work needed
Solution Approach 1:
Instead of applying quality checks to all annotations (excessive action), the patent applies quality checks only to samples from specific environment conditions where problems are identified (partial action). This selective approach maintains annotation quality for problematic cases while dramatically reducing the overall workload and increasing productivity.
Solution Approach 2:
The patent performs preliminary automated annotation using neural networks before human quality checks. This preliminary action handles the bulk of annotations automatically, and human reviewers only need to verify or correct specific problematic cases, thus maintaining quality while improving productivity.
3Productivity
If more labelers are hired to increase productivity, then annotation throughput increases, but cost and project complexity increase
Solution Approach 1:
The patent implements a self-service automated annotation system using neural networks that can process annotations with minimal human intervention. The system automatically identifies problematic cases, triggers targeted retraining, and produces high-quality annotations without requiring large teams of labelers, thus improving productivity while reducing project complexity.
4Manufacturing precision
If neural networks are retrained frequently to improve quality, then annotation quality improves, but computing power and time requirements increase
Solution Approach 1:
The patent changes the training parameter by targeting retraining only at specific environment conditions where quality problems are detected, rather than frequent comprehensive retraining. This selective parameter adjustment improves annotation quality for problematic cases while minimizing computing power consumption and training time.
Solution Approach 2:
Instead of performing complete retraining (excessive action), the patent performs partial retraining focused only on specific environment conditions where quality issues are identified. This partial action achieves quality improvement where needed while significantly reducing computing power requirements and training time.
Data Source
AI summary
A computer-implemented method for automatically annotating frames of sensor data includes: receiving the frames of sensor data; grouping the frames into a plurality of packets based on at least one condition attribute, wherein the at least one condition attribute describes at least one environment condition that existed while a respective frame of sensor data was being recorded; annotating frames from a first packet using a neural network, wherein the annotating comprises assigning at least one data point to each frame, wherein the first packet comprises frames for which the at least one condition attribute is in a selected value range; selecting a first sample of one or more frames from the first packet and determining a quality measure for data points of the first sample; and ascertaining that the quality measure for the first sample is below a predefined threshold.