ML Visual Inspection Tool Automating Model Development
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Solution Overview
Problem
The current AI visual inspection model development process is time inefficient due to the need for extensive customization and lack of a unified source code, leading to long development timelines and inefficient manpower allocation, which is exacerbated by the shortage of skilled manpower.
Innovation Solution
A machine-learning based tool for visual inspection that initializes a process using selected images, fetches historical data for model recommendations, and provides automated or semi-automatic feedback to users, optimizing alignment methods, feature extraction, and machine learning model selection, thereby reducing the need for manual expertise and speeding up the development process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If extensive customization is performed at each stage of the AI visual inspection model development process, then the model can be tailored to specific project requirements, but the development time increases significantly
Solution Approach 1:
The system performs preliminary actions by automatically fetching historical data and pre-selecting appropriate methods and parameters for each development stage based on past project patterns. This eliminates the need for operators to manually explore options at each stage, thereby reducing development time while maintaining customization capability through historically proven configurations
Solution Approach 2:
The system implements feedback mechanisms by analyzing historical project data and using it to automatically recommend optimal methods and parameters for current projects. The feedback loop continuously improves recommendations by learning from past project outcomes, enabling fast customization without manual intervention
2Reliability
If skilled machine learning experts are deployed to select the right options at each stage, then the quality of the AI model improves, but the cost and difficulty increase due to shortage of skilled manpower
Solution Approach 1:
The system enables self-service by automatically performing tasks that traditionally required skilled machine learning experts. It autonomously fetches historical data, identifies appropriate methods, selects parameters, and generates recommendations for each development stage, allowing non-expert operators to achieve expert-level results without manual intervention
Solution Approach 2:
The system acts as an intermediary between historical project data and current project decisions. It mediates the complexity by translating historical patterns into actionable recommendations, bridging the gap between available data and expert decision-making without requiring actual experts to be present
3Productivity
If a unified and universal source code is implemented across all projects, then the development process becomes faster and more efficient, but the ability to handle diverse project requirements decreases
Solution Approach 1:
The system implements universality by creating a unified platform that handles diverse project requirements through a common architecture. It maintains a universal source code base that can be automatically adapted to different projects by fetching relevant historical data and selecting appropriate configurations, thereby achieving both speed and versatility simultaneously
Data Source
AI summary
A method for developing machine-learning (ML) based tool including initializing an input dataset, which is pre-processed by a first model to harmonize the dataset. Historical data similar to the input data set is fetched from a historical database. Based thereupon a controller recommends a method and a control-setting associated with the identified model for the visual inspection process to a user. Thereafter, the dataset is annotated by a second model to define a labelled data set. A plurality of features are extracted with respect to the data set through a feature extractor. A machine-learning classifier operates upon the extracted features and classifies the dataset with respect to one or more labels. A meta controller communicates with one or more of the first model, the second model, the feature extractor and the selected classifier for assessing a performance of at least one of first model and the feature extractor.