Aquatic Life Image Curation for ML Training
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Solution Overview
Problem
Current image processing using machine learning models for aquatic life data faces challenges in efficient data annotation and storage, leading to resource wastage and potential non-compliance with data privacy regulations, particularly in managing large volumes of images and ensuring accurate labeling for rare species recognition.
Innovation Solution
An aquatic life data curation system that implements data annotation and storage rules to selectively label and store images, allowing for efficient resource allocation, improved data privacy compliance, and enhanced image relevance for tasks like fish disease detection by filtering noise and prioritizing relevant images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all aquatic life images are processed and labeled using machine learning models, then the completeness of data annotation is improved, but computing resource consumption increases
Solution Approach 1:
The system performs preliminary actions by automatically filtering and pre-processing images before they reach the machine learning modeling stage. Images are pre-sorted by quality metrics and relevance scores, so only the most valuable images are sent for full annotation, reducing overall computing resource consumption while maintaining annotation completeness for important data.
Solution Approach 2:
The system extracts and separates images into different categories based on their quality and relevance. High-quality, relevant images are extracted for full machine learning processing, while low-quality or redundant images are separated and handled differently, reducing the computational burden on the system.
2Measurement precision
If all aquatic life images are processed and labeled, then the accuracy of machine learning model fine-tuning is improved, but processing time increases
Solution Approach 1:
The system applies local quality by differentiating the processing level based on individual image characteristics. Each image is evaluated for its specific quality and relevance, and then processed accordingly - high-quality images receive full processing for maximum accuracy, while lower-quality images receive reduced processing, optimizing the balance between accuracy and time.
Solution Approach 2:
Preliminary quality assessment and filtering actions are performed before full processing. Images are pre-evaluated on quality metrics, and only those meeting certain thresholds proceed to full machine learning processing, significantly reducing total processing time while maintaining high accuracy for the final model.
3Reliability
If data privacy regulations are strictly enforced, then compliance with GDPR is improved, but data processing flexibility is reduced
Solution Approach 1:
The system performs preliminary actions to assess and verify data privacy compliance before processing begins. Images are pre-screened for privacy sensitivities and regulatory requirements, allowing the system to automatically adjust processing parameters or data handling procedures in advance, maintaining both compliance and processing flexibility.
Solution Approach 2:
The system implements dynamic data processing where parameters and procedures automatically adjust based on the specific characteristics of each image and its compliance status. This allows the system to maintain GDPR compliance while preserving processing flexibility by adapting to different data scenarios rather than using rigid fixed procedures.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing aquatic life data, e.g., aquatic life image. One of the methods includes receiving aquatic life data comprising a plurality of aquatic life images from a user through a user interface; receiving, within the user interface, a first user request to use the aquatic life data to train a machine learning model; determining a data curator score for each aquatic life image; identifying, based on the data curator scores, a proper subset of the plurality of aquatic life images; providing the proper subset of the plurality of aquatic life images to one or more data annotators; receiving annotation data generated by the one or more data annotators; and providing the annotation data to a training system configured to train the machine learning model by using the annotation data.