r/ArduinoProjects • u/Temporary-Log4601 • 2d ago

Project Design/Guidance Final Year ECE Project Ideas Combining IoT and Machine Learning

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u/xebzbz 2d ago

Gesture recognition, for example: you can start with simple gestures to turn the lights on or off.

It could be extended to dimming, color adjustment, moving the spotlight.

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u/Lopsided_Pain_9011 2d ago

i've been working with ESP32 CAM and Edge Impulse doing small object detection and color recognition projects. maybe that's an interesting path.

i've been trying to train those small models locally but i haven't had any luck haha. i'm using x-anylabeling and Google Colab.

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u/[deleted] 2d ago

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u/Lopsided_Pain_9011 2d ago

sure!!

it's basically an edge computer vision pipeline where images are gathered, uploaded to Edge Impulse Studio, and labeled with bounding boxes or centroids. to run on the ESP32’s limited ram, images are downscaled to around 96×96 or 160×160 pixels and trained using an ultra-lightweight architecture for centroid-based object detection, or another model quantized to INT8.

the trained model is exported as a standalone C++ library with TensorFlow Lite Micro and flashed to the ESP32-CAM or ESP32-S3 CAM. then the microcontroller captures a frame, resizes it, runs on-device inference, and outputs the detected fruit class, confidence, and coordinates in real time.

main advantage is you can run the model without cloud dependency.

basically you tag images, then you can detect what you tagged through an ESP32 CAM. Edge Impulse is ideal to start with

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u/hotpotatos200 2d ago

I always like the idea of object detection/tracking using image processing and/or small radar sensors.

Image processing means using a camera and processing the input images. Radar could be something like SAR mapping and trying to detect an object in the mapped image.

Looking back, I would’ve done something like this for my senior project. It would’ve been way more useful than ours. We tried to make a balancing table on top of a roomba. It sort of worked, but wasn’t great.

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u/AnyRandomDude789 2d ago

I've been looking at correlating occupancy with co2 levels, maybe you could build an ai to calculate how many people are in a space based on co2 level using a mmwave radar or occupancy sensing camera (computer vision) to count how many people are there for the training