Kutluhan Aktar / AI-assisted Pipeline Diagnostics
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About this project
This model detects pipeline diagnostic classes based on the mmWave data parameters extracted from a 60GHz mmWave radar module:
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
|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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Data collected3m 30s