MobileNet-Tiny: A Deep Neural Network-Based Real-Time Object Detection for Rasberry Pi

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI:10.1109/ICMLA.2019.00118

Nithesh Singh Sanjay, A. Ahmadinia

引用次数: 6

Abstract

In this paper, we present a new neural network architecture, MobileNet-Tiny that can be used to harness the power of GPU based real-time object detection in raspberry-pi and also in devices with the absence of a GPU and limited graphic processing capabilities such as mobile phones, laptops, etc. MobileNet-Tiny trained on COCO dataset running on a non-Gpu laptop dell XPS 13, achieves an accuracy of 19.0 mAP and a speed of 19.4 FPS which is 3 times as fast as MobileNetV2, and when running on a raspberry pi, it achieves a speed of 4.5 FPS which is up to 7 times faster than MobileNetV2. MobileNet-Tiny was modeled to offer a compact, quick, and well-balanced object detection solution to a variety of GPU restricted devices.

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MobileNet-Tiny:基于深度神经网络的Rasberry Pi实时目标检测

在本文中，我们提出了一种新的神经网络架构，MobileNet-Tiny，它可以用于利用基于GPU的树莓派实时目标检测的能力，也可以用于没有GPU和图形处理能力有限的设备，如手机，笔记本电脑等。在COCO数据集上训练的MobileNet-Tiny在非gpu笔记本电脑戴尔XPS 13上运行，达到了19.0 mAP的精度和19.4 FPS的速度，是MobileNetV2的3倍，在树莓派上运行时，它达到了4.5 FPS的速度，比MobileNetV2快了7倍。MobileNet-Tiny旨在为各种受GPU限制的设备提供紧凑、快速、平衡良好的目标检测解决方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)

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