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Performance Evaluation of Edge Computing-Based Deep Learning Object Detection

Published: 14 December 2018 Publication History

Abstract

This article presents a method for implementing the deep learning object detection based on a low-cost edge computing IoT device. The limit of the hardware is a challenge for working the pre-trained neural network model on a low-cost IoT device. Hence, we utilize the Neural Compute Stick (NCS) to accelerate the neural network model on a low-cost IoT device by its high efficiency floating-point operation. With the NCS, the low-cost IoT device can successfully work the pre-trained neural network model and become an edge computing device. The experimental results show the proposed method can effectively detect the objects based on deep learning on an edge computing IoT device. Furthermore, the objective experiment demonstrates the proposed method can immediately infer the neural network model for images in average 1.7 seconds with only one of the NCS and the neural network model can reach average 9.2 fps for the video sequences with four NCSs acceleration. In addition, the discrepancy of the neural network model between the edge device and the edge server is less than 2% mean average precision (mAP).

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Cited By

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  • (2024)Benchmarking Deep Learning Models for Object Detection on Edge Computing DevicesService-Oriented Computing10.1007/978-981-96-0805-8_11(142-150)Online publication date: 7-Dec-2024
  • (2020)Evaluation of Deep Learning Accelerators for Object Detection at the EdgeKI 2020: Advances in Artificial Intelligence10.1007/978-3-030-58285-2_29(320-326)Online publication date: 21-Sep-2020

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  1. Performance Evaluation of Edge Computing-Based Deep Learning Object Detection

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    cover image ACM Other conferences
    ICNCC '18: Proceedings of the 2018 VII International Conference on Network, Communication and Computing
    December 2018
    372 pages
    ISBN:9781450365536
    DOI:10.1145/3301326
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 14 December 2018

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    Author Tags

    1. Deep Learning
    2. Edge Computing
    3. Object Detection

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    • (2024)Benchmarking Deep Learning Models for Object Detection on Edge Computing DevicesService-Oriented Computing10.1007/978-981-96-0805-8_11(142-150)Online publication date: 7-Dec-2024
    • (2020)Evaluation of Deep Learning Accelerators for Object Detection at the EdgeKI 2020: Advances in Artificial Intelligence10.1007/978-3-030-58285-2_29(320-326)Online publication date: 21-Sep-2020

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