Extraction of Measurement Device Information on an ESP32 Microcontroller: TinyML for Image Processing

Convolutional neural networks (CNNs) have demonstrated outstanding results in various areas of computer vision (CV). This success has led to the possibility of using CV on ever smaller computing devices, giving rise to the research area TinyML, which enables ML tasks on, e.g. resource-constrained mi...

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Bibliographic Details
Published in:Procedia computer science Vol. 246; pp. 2002 - 2011
Main Authors: Paul, Jonas, Schmid, Lukas, Klaiber, Marco, Rössle, Manfred
Format: Journal Article
Language:English
Published: Elsevier B.V 2024
Subjects:
Computer Vision
ESP32
Image Regression
Microcontroller
ESP32
Computer Vision
Microcontroller
Image Regression
ISSN:1877-0509, 1877-0509
Online Access:Get full text
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Summary:Convolutional neural networks (CNNs) have demonstrated outstanding results in various areas of computer vision (CV). This success has led to the possibility of using CV on ever smaller computing devices, giving rise to the research area TinyML, which enables ML tasks on, e.g. resource-constrained microcontrollers. On this basis, we extend the scope of TinyML and present an image regression task where a self-generated dataset is introduced. We compare eight different approaches with different CNN architectures and normalization methods, with the best performing model achieving an MAE of 0.54 on an ESP-32. Furthermore, the ML models used are compared in terms of their performance when used on an ESP32 and on a PC. Finally, we present open questions and further research directions based on our results.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2024.09.670