microYOLO: Towards Single-Shot Object Detection on Microcontrollers

Deutel M, Mutschler C, Teich J (2023)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2023

Event location: Torino IT

Open Access Link: https://arxiv.org/abs/2408.15865

Abstract

This work-in-progress paper presents results on the feasibility of single-shot object detection on microcontrollers using YOLO. Single-shot object detectors like YOLO are widely used, however due to their complexity mainly on larger GPU-based platforms. We present microYOLO, which can be used on Cortex-M based microcontrollers, such as the OpenMV H7 R2, achieving about 3.5 FPS when classifying 128x128 RGB images while using less than 800 KB Flash and less than 350 KB RAM. Furthermore, we share experimental results for three different object detection tasks, analyzing the accuracy of microYOLO on them.

Authors with CRIS profile

Mark Deutel Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design) Christopher Mutschler
Jürgen Teich Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design)

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Deutel, M., Mutschler, C., & Teich, J. (2023). microYOLO: Towards Single-Shot Object Detection on Microcontrollers. In Proceedings of the 4th Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM). Torino, IT.

MLA:

Deutel, Mark, Christopher Mutschler, and Jürgen Teich. "microYOLO: Towards Single-Shot Object Detection on Microcontrollers." Proceedings of the 4th Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM), Torino 2023.

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