Object Detection with Deep Learning on Raspberry Pi
The Raspberry Pi camera uses object detection with deep learning through Tensorflow in order to detect, recognize and classify images captured with the camera.
Area of Interest
Software Engineering, AI, Data Science
Monta Vista High School
For my Second Milestone, I added Amazon’s voice control service, Alexa. I chose Alexa over Jasper because I have an Echo Dot and Alexa activated smart light bulbs at home.
My first milestone was to set up the Raspberry Pi, installing Tensorflow, and running a pretrained object detection model with Tensorflow.
I set up my Raspberry Pi 3B by installing the Raspberry Pi OS onto a microSD card using Raspberry Pi Imager, enabling SSH and VNC, and connecting the Raspberry Pi to my Wifi network through an Ethernet cable. I also applied two heat sinks to prevent the Raspberry Pi from overheating.
After connecting to my Raspberry Pi through Putty, I installed Tensorflow Lite 2 by installing a virtual environment along with NumPy and installed Tensorflow packages inside the virtual environment.
Next, I ran a pretrained model with Tensorflow. I initially connected and configured the Pi Camera, installed the Interpreter class from the Tensorflow Lite API, installed a few Python packages, and ran the example model, which displays a camera preview with the predicted image classification result and its confidence interval.