Image Classifier Using Resource-Constrained Device and Tiny Machine Learning

Image classification is one of the main parts of computer vision, which is important in applications like self-driving automotives/vehicle systems. While working with image/video data it needs huge amount of resources including computing power, graphic processing units (GPU), memory, high end CPUs, etc. We can use the small microcontrollers to do the same task, by using high-end machines for training and building the model and converting the model so that they fit into microcontroller unit, by using the method called Transfer Learning. In our work, we use Arduino nano 33 BLE sense and OV7675 camera module and online machine learning framework called Edge impulse for building the model. It is found that our tiny machine learning model works well and provides a real-time solution for image classification in the resource-constrained scenario. The experimental results show that the image classifier is performing with around 100% accuracy and so has got a wide scope in real-time classification applications.