Kutluhan Aktar / AI-driven BLE Travel Emergency Assistant
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About this project
This object detection (FOMO) model detects customized keychains (tokens) representing different emergencies:
- Fine
- Danger
- Assist
- Stolen
- Call
After building my object detection model, I deployed my model as a fully optimized and customizable Arduino library and uploaded it to XIAO ESP32S3. Also, I developed an Android application from scratch to obtain the model detection results via BLE and transfer them to a web application in order to inform emergency contacts of the detected emergency class over WhatsApp and SMS via Twilio.
Download block output
Title | Type | Size | |
---|---|---|---|
Image training data | NPY file | 60 windows | |
Image training labels | JSON file | 60 windows | |
Image testing data | NPY file | 15 windows | |
Image testing labels | JSON file | 15 windows | |
Object detection model | TensorFlow Lite (float32) | 82 KB | |
Object detection model | TensorFlow Lite (int8 quantized) | 55 KB | |
Object detection model | TensorFlow SavedModel | 186 KB | |
Object detection model | Keras h5 model | 88 KB |
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Summary
Data collected
75 itemsProject info
Project ID | 298397 |
Project version | 2 |
License | Apache 2.0 |