Learning Data Generation for Defect Identification Accuracy
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Solution Overview
Problem
Existing image identification devices struggle to achieve high accuracy in detecting various defects due to insufficient and inadequate learning data, particularly when images featuring defects that the device is not well-trained on.
Innovation Solution
A data generation apparatus and method that generates new learning data by associating corrected determination results with images or composite images, specifically addressing erroneous determinations by adding information about the presence or absence of defects, thereby improving the identification device's training and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If learning data is accumulated to improve identification accuracy, then identification accuracy is improved, but the time and resources required for data preparation and processing increase
Solution Approach 1:
The system uses the identification device's determination results as feedback to automatically generate new learning data. When the identification device makes a determination on an image, the system automatically creates corrected learning data based on that determination, eliminating the need for manual data collection and preparation while continuously improving identification accuracy through iterative learning.
Solution Approach 2:
The system enables the identification device to improve itself autonomously by generating its own learning data from its determination results. The learning data generation unit automatically creates training data using the images and determination results produced by the identification device, allowing the system to self-enhance without external intervention or additional manual data preparation.
2Adaptability or versatility
If learning data includes sufficient types of images with defects to handle various defect types, then the identification device can handle various defects, but the complexity of data collection and management increases
Solution Approach 1:
The identification device automatically generates diverse learning data covering various defect types through its own determination process. As the device processes different images and makes determinations, the system automatically creates corresponding learning data entries, enabling the device to handle various defect types without requiring complex manual data collection and management systems.
Solution Approach 2:
The system prepares learning data in advance by automatically generating it from the identification device's determination results on existing images. This preliminary generation of learning data ensures that the device is pre-trained on diverse defect types before actual inspection tasks, reducing the need for complex ongoing data management while maintaining high adaptability.
Data Source
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AI summary
Provided are a data generation apparatus, data generation method, and a data generation program for accumulating, as learning data, an image that includes a feature on which an identification device could not output a desirable identification result. A data generation apparatus includes: an acquisition unit configured to acquire a result determined by a determination unit, which uses an identification device trained using learning data, as to whether or not an object to be inspected includes a part to be detected based on an image of the object to be inspected; an evaluation unit configured to evaluate whether or not the determination result is correct; and a generation unit configured to generate, if the evaluation unit has evaluated that the determination result is not correct, new learning data by associating at least one of the image and a composite image generated based on the image with information in which the determination result is corrected.