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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.
AI-driven Plastic Surface Defect Detection via UV-exposure
About this project
This FOMO-AD visual anomaly detection model is trained on an extensive UV-applied plastic surface image dataset, showcasing various plastic material types and surface defect stages (none, high, and extreme).
While labeling the UV-applied plastic surface image samples, I needed to utilize the default classes required by Edge Impulse to enable the F1 score calculation:
- no anomaly
- anomaly
After training and validating, I deployed my FOMO-AD model as an EIM binary for Linux (AARCH64) compatible with Raspberry Pi 5.
The project GitHub repository provides:
- The extensive UV-applied plastic surface image dataset
- Code files
- PCB manufacturing files
- Mechanical part and component design files (STL)
- Edge Impulse FOMO-AD visual anomaly detection model (EIM binary for Linux AARCH64)
Run this model
On any device
Dataset summary
Data collected
6,959 itemsLabels
no anomalyProject info
| Project ID | 800518 |
| License | 3-Clause BSD |
| No. of views | 2,373 |
| No. of clones | 0 |
