Lightweight mask detection algorithm based on improved YOLOv4-tiny

Jie ZHU; Jian-li WANG; Bin WANG

doi:10.37188/CJLCD.2021-0059

您当前的位置：

首页 >

文章列表页 >

Lightweight mask detection algorithm based on improved YOLOv4-tiny

Target Detectionand Tracking | 更新时间：2021-11-08

- Lightweight mask detection algorithm based on improved YOLOv4-tiny
- Chinese Journal of Liquid Crystals and Displays Vol. 36, Issue 11, Pages: 1525-1534(2021)
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
  2.中国科学院大学, 北京 100049
- 作者简介：
- 基金信息：
  
  Supported by National Natural Science Foundation of China(11703024)
- DOI：10.37188/CJLCD.2021-0059
  CLC： TP391.41
- Received：01 March 2021，
  
  Revised：26 March 2021，
  
  Published：2021-11
- 稿件说明：
移动端阅览
Jie ZHU, Jian-li WANG, Bin WANG. Lightweight mask detection algorithm based on improved YOLOv4-tiny[J]. Chinese journal of liquid crystals and displays, 2021, 36(11): 1525-1534.
DOI：

Jie ZHU, Jian-li WANG, Bin WANG. Lightweight mask detection algorithm based on improved YOLOv4-tiny[J]. Chinese journal of liquid crystals and displays, 2021, 36(11): 1525-1534. DOI： 10.37188/CJLCD.2021-0059.

摘要

新冠疫情防控期间，为防止病毒扩散，需要在机场、车站等公共场所对人流的口罩佩戴情况实施管控。为了高效地监测人流的口罩佩戴情况，提出了一种基于YOLOv4-tiny改进的轻量级口罩检测算法。在YOLOv4-tiny的骨干网络之后引入空间金字塔池化结构，对输入特征层进行多尺度池化并融合，同时大幅增强网络的感受野。结合路径聚合网络，分两条路径将不同尺度的特征层相互融合、反复增强，以提升特征层对目标的表达能力。使用标签平滑策略优化了网络损失函数，以抑制网络训练过程中的过拟合问题。实验结果表明，该算法在口罩目标和人脸目标上的检测精度分别达到了94.7%和85.7%，相比于YOLOv4-tiny分别提高了4.3%和7.1%，实时检测速度达到76.8 FPS (on GeForce GTX 1050Ti)，满足了多种场景下口罩检测任务的检测精度与实时性要求。

Abstract

During the period of 2019-nCoV controlling

to prevent the spread of the virus

it is necessary to regulate the coverage of mask wearing in densely populated places such as airports and stations. In order to effectively monitor the coverage of mask wearing of crowd

this paper proposes a lightweight mask detection algorithm based on improved YOLOv4-tiny. Following the backbone network of YOLOv4-tiny

a spatial pyramid pooling structure is introduced to pool and fuse the input features at multi-scale

which makes the receptive field of the network enhanced. Then

combined with the path aggregation network

multi-scale features are fused and enhanced repeatedly in two paths to improve the expressive ability of feature maps. Finally

label smoothing is utilized to optimize the loss function for modifying the over-fitting problem in the training process. The experimental results show that the proposed algorithm achieves 94.7% AP and 85.7% AP on mask target and face target respectively (at real-time speed of 76.8 FPS on GeForce GTX 1050ti)

which is 4.3% and 7.1% higher than that of YOLOv4-tiny. The proposed algorithm meets the accuracy and real-time requirements of mask detection tasks in various scenes.

关键词

Keywords

references

REDMON J, DIVVALA S, GIRSHICK R, et al . You only look once: unified, real-time object detection[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas: IEEE, 2016: 779-788.

REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Honolulu: IEEE, 2017: 6517-6525.

REDMON J, FARHADI A. Yolov3: an incremental improvement[J]. arXiv : 1804.02767, 2018.

BOCHKOVSKIY A, WANG C Y, LIAO H Y M. Yolov4: optimal speed and accuracy of object detection[J]. arXiv : 2004.10934, 2020.

WANG C Y, BOCHKOVSKIY A, LIAO H Y M. Scaled-YOLOv4: scaling cross stage partial network[J]. arXiv : 2011.08036, 2020.

R GIRSHICK , J DONAHUE , T DARRELL , 等 . Region-based convolutional networks for accurate object detection and segmentation . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2016 . 38 ( 1 ): 142 - 158 . DOI: 10.1109/TPAMI.2015.2437384 http://doi.org/10.1109/TPAMI.2015.2437384 .

GIRSHICK R. Fast R-CNN[C]// Proceedings of the IEEE International Conference on Computer Vision . Santiago: IEEE, 2015: 1440-1448.

S Q REN , K M HE , R GIRSHICK , 等 . Faster R-CNN: towards real-time object detection with region proposal networks . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017 . 39 ( 6 ): 1137 - 1149 . DOI: 10.1109/TPAMI.2016.2577031 http://doi.org/10.1109/TPAMI.2016.2577031 .

A CABANI , K HAMMOUDI , H BENHABILES , 等 . MaskedFace-Net-a dataset of correctly/incorrectly masked face images in the context of COVID-19 . Smart Health , 2021 . 19 100144 DOI: 10.1016/j.smhl.2020.100144 http://doi.org/10.1016/j.smhl.2020.100144 .

WANG Z Y, WANG G C, HUANG B J, et al . Masked face recognition dataset and application[J]. arXiv : 2003.09093, 2020.

牛作东 , 覃涛 , 李捍东 , 等 . 改进RetinaFace的自然场景口罩佩戴检测算法 . 计算机工程与应用 , 2020 . 56 ( 12 ): 1 - 7 . https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202012002.htm https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202012002.htm .

Z D NIU , T QIN , H D LI , 等 . Improved algorithm of RetinaFace for natural scene mask wear detection . Computer Engineering and Applications , 2020 . 56 ( 12 ): 1 - 7 . https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202012002.htm https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202012002.htm .

王艺皓 , 丁洪伟 , 李波 , 等 . 复杂场景下基于改进YOLOv3的口罩佩戴检测算法 . 计算机工程 , 2020 . 46 ( 11 ): 12 - 22 . https://www.cnki.com.cn/Article/CJFDTOTAL-JSJC202011002.htm https://www.cnki.com.cn/Article/CJFDTOTAL-JSJC202011002.htm .

Y H WANG , H W DING , B LI , 等 . Mask wearing detection algorithm based on improved YOLOv3 in complex scenes . Computer Engineering , 2020 . 46 ( 11 ): 12 - 22 . https://www.cnki.com.cn/Article/CJFDTOTAL-JSJC202011002.htm https://www.cnki.com.cn/Article/CJFDTOTAL-JSJC202011002.htm .

LIN T Y, MAIRE M, BELONGIE S, et al . Microsoft COCO: common objects in context[C]//13 th European Conference on Computer Vision . Zurich: Springer, 2014: 740-755.

K M HE , X Y ZHANG , S Q REN , 等 . Spatial pyramid pooling in deep convolutional networks for visual recognition . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2015 . 37 ( 9 ): 1904 - 1916 . DOI: 10.1109/TPAMI.2015.2389824 http://doi.org/10.1109/TPAMI.2015.2389824 .

LIU S, QI L, QIN H F, et al . Path aggregation network for instance segmentation[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Salt Lake City: IEEE, 2018: 8759-8768.

LIN T Y, DOLLÁR P, GIRSHICK R, et al . Feature pyramid networks for object detection[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Honolulu: IEEE, 2017: 936-944.

MVLLER R, KORNBLITH S, HINTON G. When does label smoothing help?[J]. arXiv : 1906.02629, 2019.

WANG C Y, LIAO H Y M, WU Y H, et al . CSPNet: a new backbone that can enhance learning capability of CNN[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops . Seattle: IEEE, 2020: 1571-1580.

Z H ZHENG , P WANG , W LIU , 等 . Distance-IoU loss: faster and better learning for bounding box regression . Proceedings of the AAAI Conference on Artificial Intelligence , 2020 . 34 ( 7 ): 12993 - 13000 . DOI: 10.1609/aaai.v34i07.6999 http://doi.org/10.1609/aaai.v34i07.6999 .

LIU W, ANGUELOV D, ERHAN D, et al . SSD: single shot multibox detector[C]//14 th European Conference on Computer Vision . Amsterdam: Springer, 2016: 21-37.

SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv : 1409.1556, 2014.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Small object detection algorithm in UAV aerial images based on improved YOLO11

Video object tracking algorithm based on dual-branch online optimization and feature fusion

Camouflaged object segmentation based on edge enhancement and feature fusion

Object detection in foggy image based on Double-Head

Related Author

Jie ZHU

Jian-li WANG

Bin WANG

ZHANG Zhihao

LI Xiaorun

CHEN Shuhan

LI Xinpeng

WANG Peng

Related Institution

Changchun Institute of Optics， Fine Mechanics and Physics， Chinese Academy of Sciences

College of Electrical Engineering， Zhejiang University

Xi'an Institute of Electromechanical Information Technology

School of Electronics and Information Engineering， Xi'an Technological University

Department of Electric and Electronic Engineering， Shanghai University of Engineering Science

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China 130033 Postal code：130033
Tel：0431-86176059 Email：yjxs@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰