# microYOLO: Towards Single-Shot Object Detection on Microcontrollers

Source: [microYOLO: Towards Single-Shot Object Detection on Microcontrollers](<https://www.sota2.com/research/paper/68fa76771e209131cea6d2f2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:54:27.044Z

Authors: Mark Deutel, Christopher Mutschler, J\\"urgen Teich

Published: 2024-08-28T15:29:27.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2408.15865>)

[PDF](<https://arxiv.org/pdf/2408.15865.pdf>)