Kutluhan Aktar / AI-assisted Pipeline Diagnostics
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Maintenance window planned next Monday, 04/29 at 5:00AM UTC (more)
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 |
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Summary
Data collected
3m 30sProject info
| Project ID | 214371 |
| Project version | 1 |
| License | Apache 2.0 |
