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Vision AI for
Manufacturing

  Various deep learning technologies are being utilized to enhance the extent and versatility of automation within the inspection process. Specifically, deep learning-based models are employed to complement the limited flexibility and scalability of rule-based and machine learning models.

Use Cases

PCB Classification model to minimize false positive* (resulted by Machine Learning Model)

More
Issues and
Needs to Address
  • Minimizing false positive issues of ML-based models that classify normal products in the existing inspection process as defective.
  • Ensuring the stability of performance in new deep learning-based models through training techniques is necessary.
Solutions and
Applied Technologies
  • Using SNUAILAB's AutoCare, we re-trained the inspection model (SINNet, SNUAILAB INspection Network) with on-site data.
  • To compare and contrast the performance of the retrained SINNet(Snuailab Inspection Network) with the existing ML model, we applied AutoCare Edge and conducted A/B tests, visualizing the performance history of the retrained SINNet.
Results of Technology Implementation

01

The incidence of false positive* issues in existing ML models has been reduced by approximately 50% compared to the previous iteration.

* False Positive refers to a group of genuine products erroneously classified as defective by existing models.

02


AutoCare supports the stable maintenance of performance for the newly implemented SINNet-based DL model, adapting to changes in the field.

Are you curious about
SNUAILAB's AI technology
applied across various industries?
Maximize your business values
by adopting industry-specific
AI solutions.
Are you curious about SNUAILAB's AI technology applied across various industries?
Maximize your business values by adopting industry-specific AI solutions.

   sales@snuailab.ai  sales
        snuai@snuailab.ai  general

Seoul  HQ
Seoul National University, 133 Building, Automation and Systems Research Institute, 208th Floor, 1 Gwanak-ro, Gwanak-gu, Seoul Special City

Gwanggyo  R&D / Business
1202th Floor, Building A, Gwanggyo Techno Valley, Advanced Institute of Convergence Technology, 145, Gwanggyo-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do

© 2024 SNUAILAB Co., LTD. All Rights Reserved. 
Privacy policy     Terms and conditions

   sales@snuailab.ai  sales      snuai@snuailab.ai  general

Seoul  HQ  
Seoul National University, 133 Building, Automation and Systems Research Institute, 208th Floor, 1 Gwanak-ro, Gwanak-gu, Seoul Special City


Gwanggyo  R&D / Business  
1202th Floor, Building A, Gwanggyo Techno Valley, Advanced Institute of Convergence Technology, 145, Gwanggyo-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do

© 2024 SNUAILAB Co., LTD. All Rights Reserved.   Privacy policy     Terms and conditions