microYOLO: Towards Single-Shot Object Detection on Microcontrollers
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 10 references that are in the map. First posted on arXiv 28 Aug 2024.
Builds on
6 of 10 references.
References · 6
- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
- 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
- 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
- 2017To prune, or not to prune: exploring the efficacy of pruning for model compressioncited by 596
- 2019YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detectioncited by 16
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Citing papers per year: 2025: 2.
2 papers.