microYOLO: Towards Single-Shot Object Detection on Microcontrollers
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910774862020608 |
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| author | Deutel, Mark Mutschler, Christopher Teich, Jürgen |
| author_facet | Deutel, Mark Mutschler, Christopher Teich, Jürgen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15865 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | microYOLO: Towards Single-Shot Object Detection on Microcontrollers Deutel, Mark Mutschler, Christopher Teich, Jürgen Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | microYOLO: Towards Single-Shot Object Detection on Microcontrollers |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2408.15865 |