Summary of Microyolo: Towards Single-shot Object Detection on Microcontrollers, by Mark Deutel et al.
microYOLO: Towards Single-Shot Object Detection on Microcontrollers
by Mark Deutel, Christopher Mutschler, Jürgen Teich
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the feasibility of single-shot object detection on microcontrollers using YOLO (You Only Look Once). The authors aim to develop a lightweight version of YOLO that can run on smaller platforms like Cortex-M based microcontrollers, such as the OpenMV H7 R2. The proposed solution, called microYOLO, achieves approximately 3.5 frames per second when classifying 128×128 RGB images while using less than 800 KB Flash and less than 350 KB RAM. Experimental results demonstrate the accuracy of microYOLO on three object detection tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to make a special kind of computer program called YOLO work on really small computers, like those used in robots or smart cameras. Right now, these programs are too big and complicated for these tiny computers. The researchers want to change that by creating a smaller version of YOLO that can run fast enough to be useful. They made something called microYOLO that works well on these small computers and is really good at recognizing objects. |
Keywords
» Artificial intelligence » Object detection » Yolo
