Kutluhan Aktar / AI-assisted Pipeline Diagnostics
This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.
About this project
Project Description
This model detects pipeline diagnostic classes based on the mmWave data parameters extracted from a 60GHz mmWave radar module:
- Clogged
- Cracked
- Leakage
After building my neural network model, I deployed my model as an Arduino library and uploaded it to Nicla Vision. Also, I employed Nicla Vision to capture images of deformed pipes for further examination and communicate with the web application I developed to generate a pre-formatted CSV file from the stored data records and display the model detection results.
Download block output
| Title | Type | Size | |
|---|---|---|---|
| CSV Wizard config | JSON file | 388 Bytes | |
| Raw data training data | NPY file | 180 windows | |
| Raw data training labels | NPY file | 180 windows | |
| Raw data testing data | NPY file | 30 windows | |
| Raw data testing labels | NPY file | 30 windows | |
| Classifier model | TensorFlow Lite (float32) | 13 KB | |
| Classifier model | TensorFlow Lite (int8 quantized) | 5 KB | |
| Classifier model | TensorFlow SavedModel | 18 KB | |
| Classifier model | Keras h5 model | 12 KB |
Clone project
You are viewing a public Edge Impulse project. Clone this project to add data or make changes.
Run this model
Scan QR code or launch in browser
Summary
Data collected
3m 30sProject info
| Project ID | 214371 |
| Project version | 1 |
| License | Apache 2.0 |
