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Data Supplementation Technology through Image Generation.

It is necessary to gather many and diverse data to develop a robust AI model that can cope with many exceptional situations.
However, in actual manufacturing sites, the problem of data imbalance is unavoidable. But if you utilize LAONPEOPLE\'s EZ Defect technology, you can solve the data imbalance problem through deep learning image generation technology.

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Data Supplementation Technology through Image Generation. Information
Data Supplementation Technology through Defect Image Generation

To realize a balance between various defect data, the AI development periods should be matched to
the gathering time of intermittent defect data eventually, because the frequent and intermittent defects co-exist in the actual production line.
EZ Defect technology developed by LAONPEOPLE solves this data imbalance problem through deep learning image generation technology.
The GAN algorithm is used to generate a natural defect image by inserting the characteristics of the original defect as it is
in various forms of normal images, and it provides convenience by minimizing the time to learn the defect.






Technology to understand the characteristics of defects and create deformations in various forms
In the image learning process, it can generate the most realistic images by equipping the technology that learns
not only the characteristics of defect images but also the characteristics of normal images.
The image generation algorithm based on the GAN algorithm identifies the characteristics of the defect accurately,
and the algorithm for identifying the characteristics of the normal image improves the quality of the image to generate a natural defect into the normal image.




Expected effects when AI is trained with supplementing insufficient data
1. Reduce the time of the initial review phase of an AI project
Virtually generated data can be precisely tailored to the insufficient data, which is excellent for improving the accuracy of AI.
In a situation where there is insufficient data at the beginning of the project, it can help you to balance the data and build an AI inspection system quickly.
EZ Defect's defect generation technology can be useful in situations where it is difficult to predict the time required to match the ratio of the quantity of unbalanced data equally.




2. Improvement of poor performance in certain types in the deployment phase
In the deployment stage, to operate a stable system, multiple corner cases should be reflected for each type as well as for various types.
For this purpose, you can utilize EZ Defect to generate and supplement virtual data for corner case defect conditions in various situations.
Thus, you can reinforce the data learning to derive extreme performance.





Through EZ Defect, you can acquire initial data quickly and supplement various types of defects in the deployment stage.
We can make your AI inspection system on the manufacturing site fast and powerful.