Image Processing on Resource-Constrained Devices
- Authors: Dhanesh Tolia1, Sayaboina Jagadeeshwar2, Jayendra Kumar3, Pratul Arvind4, Arvind R. Yadav5
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View Affiliations Hide Affiliations1 Department of ECE, National Institute of Technology Jamshedpur, Jamshedpur, India 2 Department of ECE, National Institute of Technology Jamshedpur, Jamshedpur, India 3 Department of ECE, National Institute of Technology Jamshedpur, Jamshedpur, India 4 Dr. Akhilesh Das Gupta Institute of Technology and Management, New Delhi, India 5 Parul Institute of Engineering and Technology, Parul University, Vadodara, Gujarat, India
- Source: Futuristic Projects in Energy and Automation Sectors: A Brief Review of New Technologies Driving Sustainable Development , pp 273-292
- Publication Date: May 2023
- Language: English
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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.
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