Image Labeling Instructions for Faster Accurate ADAS Annotation
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Solution Overview
Problem
Manual annotation of real-time data for driver assistance systems is time-consuming and prone to errors, hindering efficient data processing in autonomous vehicles.
Innovation Solution
A method using pretrained machine vision language models to automatically generate labeling instructions for images, involving an instruction model and a labeling model, with mutual validation and reconstruction loss, to improve annotation accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used for real-time data, then annotation accuracy can be maintained, but processing time increases significantly and errors may occur
Solution Approach 1:
The system performs preliminary actions by pre-training instruction models and labeling models on large datasets before actual annotation tasks. The instruction model is pre-trained to understand annotation requirements, and the labeling model is pre-trained to recognize patterns, enabling fast automated annotation without manual intervention during real-time processing
Solution Approach 2:
The patent replaces the mechanical manual annotation process with an automated machine learning system. The instruction model generates natural language instructions that guide the labeling model, substituting human annotators with an automated system that processes images through neural network architectures, eliminating manual labor while maintaining consistency
2Reliability
If manual annotation is performed, then label quality can be ensured, but productivity decreases due to time-consuming processes
Solution Approach 1:
The system implements feedback mechanisms where the instruction model generates instructions based on image content, the labeling model produces annotations, and the results are evaluated against ground truth or human feedback. This closed-loop feedback ensures label quality while automating the process to increase productivity
Solution Approach 2:
The annotation system performs self-service by automatically generating instructions and producing labels without continuous human intervention. The instruction model autonomously determines what needs to be annotated, and the labeling model independently generates annotations, enabling the system to serve itself and大幅提升 productivity
3Productivity
If automated labeling is implemented, then processing speed increases, but annotation accuracy may deteriorate without proper validation
Solution Approach 1:
The instruction model serves as an intermediary between the image input and the labeling model. It generates natural language instructions that mediate the annotation process, providing semantic guidance to the labeling model to improve accuracy while maintaining automated processing speed
Solution Approach 2:
The system performs preliminary validation by having the instruction model generate instructions that inherently encode quality requirements. The labeling model is pre-trained on high-quality data, and the instruction generation process itself acts as a preliminary check to ensure accurate annotations before final output
Data Source
AI summary
The disclosure relates to a method for automatically generating a labeling instruction for at least one image by way of a system, comprising an instruction-and-labeling model. In step a), images with manual labels are passed to the instruction model. In step b), the instruction model generates labeling instructions for the images. In step c), the machine labeling model feeds these labeling instructions and automatically generates labels for the images. In step d), a reconstruction loss quantifies a discrepancy between the manual and automatic labels. In step e), the steps a) to d) are repeated until a certain number of training loops or a set reconstruction loss value is reached. If one condition from step e) is satisfied, the instruction model is applied to new images without labels from step b.2).