Image Annotation Platform With Dynamic Labeler Weighting
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Solution Overview
Problem
Current automated approaches to image annotation for autonomous vehicle control and simulation systems are inefficient and prone to mislabeling, due to variations in image quality and environmental conditions, and manual annotation is time-consuming and inconsistent.
Innovation Solution
An image annotation platform that assigns tasks to labelers, aggregates annotations, evaluates performance, and calculates payments based on quality, using a module structure that includes initial, assignment, aggregation, output, evaluation, and payment calculation modules to improve annotation accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used, then annotation accuracy can be maintained, but time consumption and cost increase significantly
Solution Approach 1:
The patent introduces automated annotation tools and algorithms as intermediaries between manual annotation processes. These tools assist human annotators by providing pre-processed suggestions, automated object detection, and validation mechanisms, thereby maintaining high accuracy while reducing the time and effort required for manual annotation.
Solution Approach 2:
The annotation process is divided into multiple stages: automated pre-annotation, human review and correction, and post-processing validation. This segmentation allows different methods to be applied at different stages, combining the speed of automation with the accuracy of manual review where it matters most.
2Productivity
If automated annotation approaches are used, then processing efficiency increases, but mislabeling errors increase
Solution Approach 1:
The system implements feedback loops where automated annotation results are continuously evaluated and refined. Performance metrics are tracked, and the system learns from errors to improve future annotations. Human annotators also provide feedback on automated results, creating a closed-loop system that improves both efficiency and accuracy over time.
Solution Approach 2:
The patent replaces purely mechanical automated annotation systems with intelligent systems that use machine learning and computer vision algorithms. These systems can understand context, handle variations in image quality and environmental conditions, and make more accurate labeling decisions while maintaining high processing efficiency.
3Reliability
If multiple labelers annotate the same image, then annotation quality can be improved through verification, but system complexity and coordination overhead increase
Solution Approach 1:
The system dynamically adjusts the annotation process based on image characteristics, task difficulty, and labeler performance. For simple images, fewer labelers are assigned; for complex or critical images, more labelers are assigned. This dynamic approach maintains high quality while avoiding unnecessary complexity for routine tasks.
Solution Approach 2:
The system changes key parameters such as the number of labelers assigned, the level of verification required, and the complexity of annotation guidelines based on image properties and task requirements. This parameter adjustment allows the system to optimize between quality and complexity for different types of annotation tasks.
Data Source
AI summary
A system and method for implementing an image annotation platform are disclosed. A particular embodiment includes: registering a plurality of labelers to which annotation tasks are assigned; assigning annotation tasks to the plurality of labelers; determining if the annotation tasks can be closed or re-assigned to the plurality of labelers; aggregating annotations provided by the plurality of labelers as a result of the closed annotation tasks; evaluating a level of performance of the plurality of labelers in providing the annotations; and calculating payments for the plurality of labelers based on the quantity and quality of the annotations provided by the plurality of labelers.