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This is a public Edge Impulse project, use the navigation bar to see all data and models in this project; or clone to retrain or deploy to any edge device.
This is a public Edge Impulse project, use the navigation bar to see all data and models in this project; or clone to retrain or deploy to any edge device.
LoRa & LLM-enabled Drive-through Kiosk AptilTag Detection
About this project
This FOMO (Faster Objects, More Objects) object detection model is trained on unique AprilTag sign images to identify food prep stations while running the automatic web-enabled food delivery system.
While labeling the AprilTag sign image samples, I simply applied the food prep station numbers to which they are assigned:
- station_1
- station_2
- station_3
- station_4
- station_5
- station_6
After training and validating, I deployed my FOMO model as an Arduino library compatible with Arduino Nicla Vision.
The project GitHub repository provides:
- Drive-restaurant web application
- Kiosk customer endpoint code files
- Food delivery system code files
- Endpoint PCB manufacturing files (Gerber)
- Delivery system Flex PCB manufacturing files (Gerber)
- 3D part and component design files (STL)
- Edge Impulse FOMO object detection models (Arduino library)
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Dataset summary
Data collected
120 itemsLabels
station_1, station_2, station_3, station_4, station_5, station_6Project info
| Project ID | 733889 |
| Project version | 1 |
| License | 3-Clause BSD |
| No. of views | 1,437 |
| No. of clones | 0 |
