Edge Impulse Experts / AI-Based Mechanical Anomaly Detector (Camera) Public

Edge Impulse Experts / AI-Based Mechanical Anomaly Detector (Camera)

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Object detection
DFRobot FireBeetle 2 ESP32-S3 mechanical anomaly PCB industrial Twilio SMS

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.

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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 TensorFlow SavedModel 188 KB
Object detection model Keras h5 model 90 KB
Object detection model Model evaluation metrics (JSON file) -

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Summary

Data collected
45 items

Project info

Project ID 338236
Project version 1
License Apache 2.0