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.
@article{arxiv.2408.15865,
title = {microYOLO: Towards Single-Shot Object Detection on Microcontrollers},
author = {Mark Deutel and Christopher Mutschler and Jürgen Teich},
journal= {arXiv preprint arXiv:2408.15865},
year = {2025}
}
Comments
Published at the ECML PKDD Conference 2023, at the 4th Workshop on IoT, Edge, and Mobile for Embedded Machine Learning