|本期目录/Table of Contents|

[1]卢 翔,苏 杨,余 萱,等.基于深度学习的机房人物重识别研究[J].工业仪表与自动化装置,2021,(02):104-107.[doi:10.19950/j.cnki.cn61-1121/th.2021.02.024]
　LU Xiang,SU Yang,YU Xuan,et al.Research on computer room character recognition based on deep learning[J].Industrial Instrumentation & Automation,2021,(02):104-107.[doi:10.19950/j.cnki.cn61-1121/th.2021.02.024]
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基于深度学习的机房人物重识别研究

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2021年02期
页码:
104-107
栏目:
出版日期:
2021-04-15

文章信息/Info

Title:
Research on computer room character recognition based on deep learning
作者:
卢 翔1; 苏 杨1; 余 萱1; 张少超2
1.贵州电网有限责任公司 信息中心；
2.贵州大学 机械工程学院，贵州 贵阳 550002
Author(s):
LU Xiang1; SU Yang1; YU Xuan1; ZHANG Shaochao2
1. Information center of Guizhou Power Grid Co., Ltd.;
2. Mechanical engineering of Guizhou University, Guizhou Guiyang 550002, China
关键词:
深度学习; 人物重识别; 残差神经网络; 三元损失函数
Keywords:
deep learning;  character recognition;  residual neural network;  ternary loss function
分类号:
TP391
DOI:
10.19950/j.cnki.cn61-1121/th.2021.02.024
文献标志码:
A
摘要:
机房内的各类设施通常由管理员统一调度，对视频中管理人员的路径追踪至关重要。为此该文提出一种基于深度学习的机房人物重识别方法，以为后续的责任追查过程提供依据。该方法以残差神经网络ResNet-50作为特征提取网络，并使用三元损失函数使模型更适用于人物重识别任务。以贵州省某电网系统机房作为试验平台进行测试。结果表明，该方法的准确率与召回率均满足实际要求，具备一定的工程参考价值。
Abstract:
All kinds of facilities in the computer room are usually dispatched by the administrator, which is very important for the administrator’s path tracking in the video. In order to provide the basis for the follow-up process of responsibility tracing, a new method based on deep learning is proposed. In this method, the residual neural network resnet-50 is used as the feature extraction network, and the ternary loss function is used to make the model more suitable for human re recognition task. A power grid system room in Guizhou Province is used as the test platform for testing. The results show that the accuracy and recall rate of the method meet the actual requirements, and have certain engineering reference value.

参考文献/References:

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[2]宋婉茹,赵晴晴,陈昌红,等. 行人重识别研究综述[J].智能系统学报,2017,12(06):770-780.
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[7] 李彦冬,郝宗波,雷航. 卷积神经网络研究综述[J]. 计算机应用，2016,36（09）： 2508-2515.
[8] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. nature, 2015, 521(7553): 436.
[9] LITJENS G, KOOI T, BEJNORDI B E, et al. A survey on deep learning in medical image analysis[J]. Medical image analysis, 2017, 42: 60-88.
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[12] LUO H, JIANG W, GU Y , et al. A strong baseline and batch normalization neck for deep person re-identification[J]. IEEE Transactions on Multimedia, 2019(99):1-1.

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备注/Memo

备注/Memo:
收稿日期：2020-08-28

作者简介：
卢翔（1984），男，硕士研究生，高级工程师，研究方向为信息化发展。
更新日期/Last Update: 1900-01-01