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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 Vehicle Detection
About this project
This FOMO (Faster Objects, More Objects) object detection model is trained on various car replica (vehicle) images to authorize customer accounts on the restaurant web application via vehicle detection (opt-in).
While labeling the vehicle image samples, I simply applied the unique account authentication keys produced by the restaurant web application:
- a1dd
- a2f9
- a4c7
- a9f1
After training and validating, I deployed my FOMO model as an Arduino library compatible with the onboard RP2040 of RA-08H LoRaWAN node board.
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
80 itemsLabels
a1dd, a2f9, a4c7, a9f1Project info
| Project ID | 734202 |
| Project version | 1 |
| License | 3-Clause BSD |
| No. of views | 83 |
| No. of clones | 0 |
