[1]刘 霞.基于 SE Detection Net 的安全帽检测方法[J].计算机技术与发展,2020,30(06):156-159.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 030]
　LIU Xia.Safety Helmet Detection Method Based on SE Detection Net[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(06):156-159.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 030]
点击复制

基于 SE Detection Net 的安全帽检测方法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年06期
页码:
156-159
栏目:
应用开发研究
出版日期:
2020-06-10

文章信息/Info

Title:
Safety Helmet Detection Method Based on SE Detection Net
文章编号:
1673-629X(2020)06-0156-04
作者:
刘 霞
中国石油大学(华东) 计算机与通信工程学院,山东 青岛 266580
Author(s):
LIU Xia
School of Computer and Communication Engineering,China University of Petroleum,Qingdao 266580,China
关键词:
多尺度安全帽检测; 特征融合; 特征金字塔; 分辨率增强模块; 卷积神经网络
Keywords:
multi-scale safety helmet detection; feature fusion; feature pyramid; resolution enhancement module; convolutional neural network
分类号:
TP301
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 06. 030
摘要:
在日常的生产中,安全帽对工人的安全提供了保障。 为了减少因未佩戴安全帽而引起的安全事故的发生,安全帽的识别在工人安全生产方面具有极高的应用价值。 针对利用传统的机器学习方法对安全帽的检测效果不理想的问题,提出一种全卷积深度神经网络:分辨率增强检测网络,对工人是否佩戴安全帽进行检测。 该方法利用 VGG16 网络中的三个不同层次的特征图,采用提出的分辨率增强模块,使三个特征图的分辨率达到一致;将此三个特征图根据通道数相连的方式进行融合;利用融合后的特征图生成特征金字塔,用于多尺度安全帽的检测。 实验表明,相比于常见的神经网络检测模型,此方法不仅实现了实时检测安全帽的速度要求,同时具有较高的检测准确率,提高了小尺度安全帽的召回率和大尺度安全帽的检测精度。
Abstract:
In the daily production,the safety of the workers is guaranteed by the safety helmet. In order to reduce the occurrence of safety accidents caused by not wearing safety helmet,the identification of safety helmet has a high application value in the safety production of workers. Aiming at the problem that the traditional machine learning method is not ideal for safety helmet detection,a full convolution depth neural network, resolution enhanced detection network, is proposed to detect whether workers wear safety helmet or not. This method uses three different levels of feature maps in VGG16 network and the proposed resolution enhancement module to achieve the same resolution of the three feature maps. The three feature maps are fused by connecting the number of channels. The fused feature maps are used to generate feature pyramids for multi-scale safety helmet detection. Experiments show that compared with common neuralnetwork detection model,this method not only achieves the speed requirement of real-time detection of safety helmet,but also has high detection accuracy,and improves the recall rate of small-scale safety helmet and the detection accuracy of large-scale safety helmet.

相似文献/References:

[1]周伟 武港山.基于显著图的花卉图像分类算法研究[J].计算机技术与发展,2011,(11):15.
　ZHOU Wei,WU Gang-shan.Research on Saliency Map Based Flower Image Classification Algorithm[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2011,(06):15.
[2]黎粤华,单磊,田仲富,等. 基于多特征融合的视频烟雾检测[J].计算机技术与发展,2016,26(01):129.
　LI Yue-hua,SHAN Lei,TIAN Zhong-fu,et al. Video Smoke Detection Based on Multi Feature Fusion Technology[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2016,26(06):129.
[3]刘加运,李玉惠,李勃,等. 一种多维特征融合的车辆对象同一性匹配方法[J].计算机技术与发展,2016,26(04):167.
　LIU Jia-yun,LI Yu-hui,LI Bo,et al. A Vehicle Object Identity Matching Method of Multidimensional Feature Combination[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2016,26(06):167.
[4]陈浩翔,蔡建明,刘铿然,等. 手写数字深度特征学习与识别[J].计算机技术与发展,2016,26(07):19.
　CHEN Hao-xiang,CAI Jian-ming,LIU Keng-ran,et al. Deep Learning and Recognition of Handwritten Numeral Features[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2016,26(06):19.
[5]张雅倩,曾卫明,石玉虎.基于特征融合与稀疏表示的人耳识别[J].计算机技术与发展,2017,27(12):7.
　ZHANG Ya-qian,ZENG Wei-min,SHI Yu-hu.Ear Recognition Based on Feature Fusion and Sparse Representation[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2017,27(06):7.
[6]谭程午,夏利民,王 嘉.基于融合特征的群体行为识别[J].计算机技术与发展,2018,28(01):17.[doi:10.3969/ j. issn.1673-629X.2018.01.004]
　TAN Cheng-wu,XIA Li-min,WANG Jia.Recognition of Human Group Action Based on Fusion Features[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2018,28(06):17.[doi:10.3969/ j. issn.1673-629X.2018.01.004]
[7]王 敏,陈立潮,曹建芳,等.Hadoop 下自适应随机权值多特征融合图像分类[J].计算机技术与发展,2018,28(11):30.[doi:10.3969/ j. issn.1673-629X.2018.11.007]
　WANG Min,CHEN Li-chao,CAO Jian-fang,et al.Multi-feature Fusion Image Classification of Adaptive Random Weight Based on Hadoop[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2018,28(06):30.[doi:10.3969/ j. issn.1673-629X.2018.11.007]
[8]韩欣欣,叶奇玲.基于 SIFT 和 HOG 特征融合的人体行为识别方法[J].计算机技术与发展,2019,29(06):71.[doi:10. 3969 / j. issn. 1673-629X. 2019. 06. 015]
　HAN Xin-xin,YE Qi-ling.Human Action Recognition Based on Feature Fusion of SIFT and HOG[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2019,29(06):71.[doi:10. 3969 / j. issn. 1673-629X. 2019. 06. 015]
[9]宋相法,吕 明.融合三维骨架和深度图像特征的人体行为识别[J].计算机技术与发展,2019,29(07):55.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 011]
　SONG Xiang-fa,LYU Ming.Human Activity Recognition Based on Fusing 3D Skeleton and Depth Image Feature[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2019,29(06):55.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 011]
[10]王泽泓,刘厚泉.基于迁移学习与自适应特征融合的建筑物识别[J].计算机技术与发展,2019,29(12):40.[doi:10. 3969 / j. issn. 1673-629X. 2019. 12. 007]
　WANG Ze-hong,LIU Hou-quan.Building Recognition Based on Transfer Learning and Adaptive Feature Fusion[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2019,29(06):40.[doi:10. 3969 / j. issn. 1673-629X. 2019. 12. 007]

更新日期/Last Update: 2020-06-10