Blank Test Card Defect Classification via Synthetic Image Translation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Blank test cards often contain defects that can interfere with diagnostic testing equipment's ability to process them correctly, leading to incorrect readings or waste, as it is difficult to determine pre-use whether a card will be defective.
Innovation Solution
A computing device uses image-to-image translation models to generate synthetic images of blank test cards, which are then classified to determine if they will interfere with the testing process, allowing for pre-use quality assessment and preventing the use of defective cards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blank test cards are used without pre-testing, then testing productivity is maintained, but defects may interfere with diagnostic testing causing incorrect readings or waste
Solution Approach 1:
The system performs preliminary testing of blank test cards by capturing images and analyzing them for defects before the cards are used for actual diagnostic testing. This pre-screening process identifies potential interference issues in advance, allowing users to discard defective cards before they cause incorrect readings or waste during actual testing.
Solution Approach 2:
The system creates synthetic images of blank test cards by applying image-to-image translation models to captured images. These synthetic images replicate what the test card will look like after processing, allowing the system to predict potential interference issues without physically processing the actual cards, thus maintaining productivity while improving reliability.
2Difficulty of detecting and measuring
If defect detection is performed using traditional methods, then simplicity is maintained, but the ability to predict interference before use is insufficient
Solution Approach 1:
The system replaces traditional mechanical or visual inspection methods with an automated image processing system. A camera captures images of blank test cards, and computer vision algorithms analyze these images to detect defects. This substitution provides more accurate and reliable defect detection while maintaining simplicity through automation.
Solution Approach 2:
The system introduces synthetic images as an intermediary between the captured blank card images and the final interference prediction. The image-to-image translation model generates synthetic images that simulate the test card appearance after processing, serving as a mediator that enables accurate prediction of interference issues without directly analyzing the actual test card chemistry.
3Loss of substance
If test cards with potential defects are used, then resource utilization is maximized, but incorrect readings or waste may result
Solution Approach 1:
The system provides feedback to users about the quality of blank test cards before they are used for testing. By analyzing captured images and generating predictions about potential interference, the system informs users whether to proceed with testing or discard the card. This feedback mechanism prevents both the use of defective cards that would cause incorrect readings and the waste of usable cards.
Data Source
AI summary
An input image is received from testing equipment. One or more synthetic images are generated by applying an image-to-image translation model to the input image. Based on the one or more synthetic images. a binary classifier is applied to determine a classification for the received input image.