Welcome to Edge Impulse, the largest community of edge AI developers!
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-assisted Pipeline Diagnostics
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.
Run this model
Scan QR code or launch in browser
Dataset summary
Data collected
3m 30sSensors
p_1, p_2, p_3, p_4, p_5, p_6, p_7 @ 1HzLabels
Clogged, Cracked, LeakageProject info
| Project ID | 214371 |
| Project version | 1 |
| License | Apache 2.0 |
| No. of views | 31,367 |
| No. of clones | 4 |
