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:2019,28(5):238-243
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嵌入式设备高效卷积神经网络的电力设备检测
(中国石油大学(华东) 计算机与通信工程学院, 青岛 266580)
Efficient Convolutional Neural Networks for Electrical Equipment Inspection on Embedded Devices
(College of Computer & Communication Engineering, China University of Petroleum, Qingdao 266580, China)
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投稿时间:2018-12-07    修订日期:2018-12-25
中文摘要: 随着大型图像集的出现以及计算机硬件尤其是GPU的快速发展,卷积神经网络(CNN)已经成为人工智能领域的一种成功算法,在各种机器学习任务中表现出色.但CNN的计算复杂度远高于传统算法,嵌入式设备上有限资源的限制成为制造高效嵌入式计算的挑战性问题.在本文中,我们提出了一种基于嵌入式设备的高效卷积神经网络用于电力设备检测,根据处理速度评估这种高效的神经网络.结果表明,该算法能够满足嵌入式设备实时视频处理的要求.
Abstract:With the emergence of large image sets and the rapid development of computer hardware especially GPU, Convolutional Neural Network (CNN) has become a successful algorithm in the region of artificial intelligence and exhibit remarkable performance in various machine learning tasks. But the computation complexity of CNN is much higher than traditional algorithms, however, the restrict of limited resources on embedded devices become a challenging issue for making efficient embedded computing. In this study, we propose a efficient convolutional neural networks based on embedded devices for electrical equipment inspection, this efficient neural network is evaluated in term of processing speed. The results show that the proposed algorithm can meet the requirement of real-time video processing on embedded devices.
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林唯贤.嵌入式设备高效卷积神经网络的电力设备检测.计算机系统应用,2019,28(5):238-243
LIN Wei-Xian.Efficient Convolutional Neural Networks for Electrical Equipment Inspection on Embedded Devices.COMPUTER SYSTEMS APPLICATIONS,2019,28(5):238-243

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