BED: A Real-Time Object Detection System for Edge Devices

Guanchu Wang, Zaid Pervaiz Bhat, Zhimeng Jiang, Yi-Wei Chen, Daochen Zha +6 more
2/14/2022
cs.CVcs.AIcs.LG

Official code: datamllab/BED_main (60 stars, last commit 2022-11-30). 1 more implementation below.

Abstract

Deploying deep neural networks~(DNNs) on edge devices provides efficient and effective solutions for the real-world tasks. Edge devices have been used for collecting a large volume of data efficiently in different domains. DNNs have been an effective tool for data processing and analysis. However, designing DNNs on edge devices is challenging due to the limited computational resources and memory. To tackle this challenge, we demonstrate Object Detection System for Edge Devices~(BED) on the MAX78000 DNN accelerator. It integrates on-device DNN inference with a camera and an LCD display for image acquisition and detection exhibition, respectively. BED is a concise, effective and detailed solution, including model training, quantization, synthesis and deployment. The entire repository is open-sourced on Github, including a Graphical User Interface~(GUI) for on-chip debugging. Experiment results indicate that BED can produce accurate detection with a 300-KB tiny DNN model, which takes only 91.9 ms of inference time and 1.845 mJ of energy. The real-time detection is available at YouTube.

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Code Implementations(2)

datamllab/BED_mainOfficial100%

BED: A Real-Time Object Detection System for Edge Devices

608Shell, PythonFeb 13, 20223 years ago
datamllab/BED_GUIOfficial100%
41C, MakefileFeb 13, 20224 years ago

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Cite this paper

@article{wang2022bed,
  title  = {BED: A Real-Time Object Detection System for Edge Devices},
  author = {Guanchu Wang and Zaid Pervaiz Bhat and Zhimeng Jiang and Yi-Wei Chen and Daochen Zha and Alfredo Costilla Reyes and Afshin Niktash and Gorkem Ulkar and Erman Okman and Xuanting Cai and Xia Hu},
  year   = {2022},
  eprint = {2202.07503},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2202.07503}
}

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