Edge Impulse Experts / AI-Based Mechanical Anomaly Detector (Camera)
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About this project
This object detection (FOMO) model detects specialized components (color-coded) representing defective parts causing mechanical deviations in a production line:
- Red
- Green
- Blue
After building my object detection model, I deployed my model as a fully optimized and customizable Arduino library and uploaded it to FireBeetle 2 ESP32-S3. Also, I developed a web application from scratch to inform the user of the diagnosed root cause of the inflicted anomaly via SMS through Twilio.
Download block output
| Title | Type | Size | |
|---|---|---|---|
| Image training data | NPY file | 39 windows | |
| Image training labels | JSON file | 39 windows | |
| Image testing data | NPY file | 6 windows | |
| Image testing labels | JSON file | 6 windows | |
| Object detection model | TensorFlow Lite (float32) | 83 KB | |
| Object detection model | TensorFlow Lite (int8 quantized) | 55 KB | |
| Object detection model | Model evaluation metrics (JSON file) | 4 KB | |
| Object detection model | TensorFlow SavedModel | 188 KB | |
| Object detection model | Keras h5 model | 90 KB |
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Summary
Data collected
45 itemsProject info
| Project ID | 338236 |
| Project version | 1 |
| License | Apache 2.0 |
