Grid Image Authentication for Crowdsourced Detection Annotation
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Solution Overview
Problem
Annotating large datasets for object detection models is time-consuming and costly, hindering the scalability and continuous optimization of these models, especially in federated learning environments.
Innovation Solution
A crowdsourcing framework using grid image user authentication systems, such as CAPTCHAs, to collect annotation data for images with detection errors, converting images into grid formats for user input to identify object locations, and using this data to update the object detection models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation of images is performed to improve object detection model accuracy, then the model performance is improved, but the time consumption and cost increase significantly
Solution Approach 1:
The system allows users to authenticate themselves through image annotation tasks, turning the annotation process into a self-service mechanism where users prove their humanity by correctly identifying objects in images. This eliminates the need for separate manual annotation workflows and integrates annotation into the authentication process itself.
Solution Approach 2:
The same image dataset serves dual purposes: both for training object detection models and for user authentication (CAPTCHA). By making the annotation system universal, it simultaneously improves model accuracy while collecting training data, resolving the contradiction between accuracy improvement and time consumption.
2Measurement precision
If centralized annotation processes are used to ensure quality control, then annotation accuracy is maintained, but scalability and speed of model updates are reduced
Solution Approach 1:
The annotation process is segmented and distributed across multiple users performing authentication tasks. Instead of a centralized annotation team, the workload is divided into many small authentication tasks performed by different users, enabling parallel processing and rapid data collection while maintaining quality through the inherent verification of human authentication.
Solution Approach 2:
The system enables continuous collection of annotation data through ongoing user authentication activities. As users continuously authenticate themselves, annotation data is continuously collected and can immediately be used for model training and updates, eliminating the batch processing delays of centralized annotation and enabling continuous model improvement.
3Reliability
If large datasets are annotated to improve model robustness, then model reliability increases, but the cost and complexity of the annotation process increase
Solution Approach 1:
The authentication system and annotation system are merged into a single universal process. The same interface and user actions serve both authentication and annotation purposes, eliminating the need for separate annotation tools and reducing system complexity while enabling large-scale data collection for improved model robustness.
Data Source
AI summary
Techniques for automating image annotation for machine model updating are described. An example, computer implemented method comprises collecting images processed by an object detection model and associated with a detection error by the object detection model, wherein the object detection model is configured to detect respective objects in the images having a defined criterion. The method further comprises converting the images into grid images comprising a plurality of cells, providing the grid images to an authentication system that employs the grid images in association with authenticating users based on reception of user input selecting respective cells of the grid images depicting an object having the defined criterion, and receiving the grid images from the authentication system with annotation data associated therewith generated based on the user input, the annotation data identifying the respective cells of the grid images depicting the object having the defined criterion.