[1]程显毅,胡海涛,季国华,等.基于深度学习监控场景下的多尺度目标检测算法研究[J].南京师范大学学报(工程技术版),2018,18(03):033.[doi:10.3969/j.issn.1672-1292.2018.03.005]
　Cheng Xianyi,Hu Haitao,Ji Guohua,et al.Research on Algorithm of Multi-Scale Target DetectionBased on Deep Learning in Monitoring Scenario[J].Journal of Nanjing Normal University(Engineering and Technology),2018,18(03):033.[doi:10.3969/j.issn.1672-1292.2018.03.005]
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基于深度学习监控场景下的多尺度目标检测算法研究
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
18卷
期数:
2018年03期
页码:
033
栏目:
人工智能算法与应用专栏
出版日期:
2018-09-30

文章信息/Info

Title:
Research on Algorithm of Multi-Scale Target DetectionBased on Deep Learning in Monitoring Scenario
文章编号:
1672-1292(2018)03-0033-06
作者:
程显毅1; 2; 胡海涛2; 季国华1; 孙丽丽1
(1.硅湖职业技术学院计算机系,江苏 昆山 215323)(2.南通大学南通先进通信技术研究院,江苏 南通 226019)
Author(s):
Cheng Xianyi1; 2; Hu Haitao2; Ji Guohua1; Sun Lili1
(1.Department of Computer,Silicon Lake Vocational and Technical College,Kunshan 215323,China)(2.Nantong Research Institute for Advanced Communication Technologies,Nantong University,Nantong 226019,China)
关键词:
深度学习; 目标检测; 空洞卷积核; 监控场景
Keywords:
deep learning; target detection; dilated kernelof convolution; monitoring scenarios
分类号:
TP181
DOI:
10.3969/j.issn.1672-1292.2018.03.005
文献标志码:
A
摘要:
针对监控环境下的视频图像处理存在漏检这一问题,分析现有目标检测算法中普遍使用的深度学习方法—Faster R-CNN,在VGG16卷积神经网络基础上,对深度卷积神经网络进行改进,在第一层卷积层中加入空洞卷积核,扩展神经网络的宽度,使得目标检测模型具有尺度不变性. 在深度学习平台PyTorch下对Cifar-10数据集进行了实验,实验结果显示,改进的目标检测算法具有较好的尺度不变性,在监控场景下更具优势.
Abstract:
In view of a problem of missed inspection in the video image processing under the monitoring environment,we analyze the deep learning method commonly used in existing target detection algorithms-Faster R-CNN,and improve a deep convolution neural network based on VGG16 convolution neural network. Expanding the width of the neural network,by adding an empty core to the first volume layer,makes the target detection model have scale invariance. The Cifar-10 dataset is tested on the in-depth learning platform PyTorch. The experimental results show that the improved target detection algorithm has a better scale invariance and has more advantages in the monitoring scene.

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备注/Memo

备注/Memo:
收稿日期:2018-04-18.
基金项目:国家自然科学基金(61771265)、江苏省现代教育技术研究课题(2017-R-54131)、南通大学-南通智能信息技术联合研究中心开放课题(KFKT2016B06).
通讯联系人:程显毅,博士,教授,研究方向:计算机视觉. E-mail:xycheng@ntu.edu.cn
更新日期/Last Update: 2018-09-30