Difference between revisions of "ML-TN-003 — AI at the edge: visual inspection of assembled PCBs for defect detection — Part 1"
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In [ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_1 this series] of articles, different embedded platforms suited for building "[[https://towardsdatascience.com/will-edge-ai-be-the-ml-architecture-of-the-future-42663d3cbb5 Edge AI]]" solutions are compared from the point of view of inferencing capabilities/features, development tools, etc. In principle, such platforms can drive a bunch of different applications in the industrial world and other fields as well. | In [ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_1 this series] of articles, different embedded platforms suited for building "[[https://towardsdatascience.com/will-edge-ai-be-the-ml-architecture-of-the-future-42663d3cbb5 Edge AI]]" solutions are compared from the point of view of inferencing capabilities/features, development tools, etc. In principle, such platforms can drive a bunch of different applications in the industrial world and other fields as well. | ||
| − | This series of Technical Notes illustrates a feasibility study regarding a common problem in the manufacturing realm that | + | This series of Technical Notes illustrates a feasibility study regarding a common problem in the manufacturing realm that supposedly Machine Learning (ML) algorithms can address effectively: defect detection by automatic visual inspection. More specifically, this study deals with the inspection of assembled Printed Circuit Boards (PCBs). The aim is to determine if it would be possible to design innovative machines exploiting ML algorithms and able to ouperform traditional devices employed for this task. |
| + | |||
| + | ==Useful links== | ||
| + | *Young-Gyu Kim, Tae-Hyoung Park, ''SMT Assembly Inspection Using Dual-Stream Convolutional Networks and Two Solder Regions'', July 2020. | ||
Revision as of 15:51, 11 March 2021
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Contents
History[edit | edit source]
| Version | Date | Notes |
|---|---|---|
| 1.0.0 | March 2021 | First public release |
Introduction[edit | edit source]
In [ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_1 this series] of articles, different embedded platforms suited for building "[Edge AI]" solutions are compared from the point of view of inferencing capabilities/features, development tools, etc. In principle, such platforms can drive a bunch of different applications in the industrial world and other fields as well.
This series of Technical Notes illustrates a feasibility study regarding a common problem in the manufacturing realm that supposedly Machine Learning (ML) algorithms can address effectively: defect detection by automatic visual inspection. More specifically, this study deals with the inspection of assembled Printed Circuit Boards (PCBs). The aim is to determine if it would be possible to design innovative machines exploiting ML algorithms and able to ouperform traditional devices employed for this task.
Useful links[edit | edit source]
- Young-Gyu Kim, Tae-Hyoung Park, SMT Assembly Inspection Using Dual-Stream Convolutional Networks and Two Solder Regions, July 2020.
