Image Processing on Resource-Constrained Devices

Abstract

The chapter portrays a new development in the field of embedded systems. It showcases the combination of Machine Learning algorithms and low-memory microcontrollers (ESP32-CAM). The uniqueness of this idea lies in the fact that Machine Learning is generally perceived as a processor-intensive task that requires high memory and storage. However, as seen in this chapter, one may soon realize how wrong this notion is with emerging technologies that are taking over the globe. This project portrays the successful implementation of a binary colour classification model on the ESP32-CAM with 68% accuracy post-training result with a mere 15 images of each colour. Machine learning has increased over the years. Some applications include image classification, object detection, and question-answering. This work merely puts out awareness in this domain and is hopeful that dedicated efforts towards it can solve many industrial problems.&nbsp;<br>