Improved YOLOv4-tiny Epidemic Collaborative Mask Wearing Detection Method

doi:10.3778/j.issn.1002-8331.2204-0473

Abstract

Abstract: The wearing of masks plays an extremely important role in epidemic control. In view of the problem of poor real-time detection of mask wearing in large-scale crowds and difficult to deploy, an modified YOLOv4-tiny epidemic collaborative mask wearing detection method is put forward. The algorithm is based on YOLOv4-tiny, and replaces the CSP modules with Resblock-D modules, which reduces the complexity of the feature extraction network and improves the detection speed; the introduction of SPP increases the receptive field of the network, so that the network can meet the image input of any size and enhance the robustness of the algorithm; it introduces a two-layer CA attention mechanism to improve the utilization of the algorithm to ensure the detection accuracy. Compared with the traditional algorithm YOLOv4-tiny, the experimental rasults reveals that the proposed network mAP is improved by 0.5 percentage points to 94.0%, and the detection speed is improved by 3.96 FPS. On the basis of making ensure a small promotion in detection accuracy, the detection accuracy is effectively increased, and the overall performance is improved.

Key words: target detection, YOLOv4-tiny, mask testing, coordinate attention（CA）, spatial pyramid pooling, Resblock-D

摘要： 口罩的佩戴对于疫情防控起着极其重要的作用，针对大规模人群下口罩佩戴检测实时性欠佳、难以部署的问题，提出了一种改进YOLOv4-tiny的疫情协同口罩佩戴检测方法。该算法以YOLOv4-tiny为基础，用两个Resblock-D模块替代CSP模块，降低特征提取网络复杂度，提升检测速度；引入SPP，增加了网络的感受野，使网络满足任意尺寸的影像输入，并增强算法的鲁棒性；引入两层CA注意力机制，提高算法的利用率以保证检测精确度。通过实验检测结果表明，相较于原始YOLOv4-tiny，所提网络mAP提升了0.5个百分点，达到94.0%，检测速度提升了3.96?FPS。在保证检测速度有少量提升的基础上有效提高了检测速度，综合性能得到提升。

关键词: 目标检测, YOLOv4-tiny, 口罩检测, 注意力机制（CA）, 空间金字塔池化, Resblock-D

CHENG Haoran, WANG Xintao, LI Junran, GUO Ziyi, LIU Wei. Improved YOLOv4-tiny Epidemic Collaborative Mask Wearing Detection Method[J]. Computer Engineering and Applications, 2023, 59(20): 208-218.

程浩然, 王薪陶, 李俊燃, 郭子怡, 刘维. 改进YOLOv4-tiny的疫情协同口罩佩戴检测方法[J]. 计算机工程与应用, 2023, 59(20): 208-218.

References

[1] LIU W，ANGUELOV D，ERHAND，et al.SSD：single shot multibox detector[J].arXiv：1512.02325，2015.
[2] 周俊宇，赵艳明.卷积神经网络在图像分类和目标检测应用综述[J].计算机工程与应用，2017，53（13）：34-41.
ZHOU J Y，ZHAO Y M.Application of convolution neural network in image classification and object detection[J].Computer Engineering and Applications，2017，53（13）：34-41.
[3] GIRSHICK R，DONAHUE J，DARRELL T，et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition，2014：580-587.
[4] GIRSHICK R.Fast R-CNN[C]//Proceedings of IEEE International Conference on Computer Vision，2015：1440-1448.
[5] REN S，HE K，GIRSHICK R，etal.FasterR-CNN：towards real-time object detection with region proposal networks[C]//Advances in Neural lnformation Processing Systems，2015.
[6] REDMON J，FARHADI A.YOLO9000：better，faster，stronger[C]//IEEE Conference on Computer Vision and Pattern Recognition，2017：6517-6525.
[7] 周华平，王京，孙克雷.改进的YOLOv4tiny行人检测算法研究[J].无线电通信技术，2021，47（4）：474-480.
ZHOU H P，WANG J，SUN K L.Pedestrian detection algorithm based on improved YOLOv4-tiny[J].Radio Communications Technology，2021，47（4）：474-480.
[8] REDMON J，FARHADI A.YOLOv3：an incremental improvement[J].arXiv：1804.02767，2018.
[9] BOCHKOVSKIY A，WANG C Y，LIAO H Y M.YOLOv4：optimal speed and accuracy of object detection[J].arXiv：2004.10934，2020.
[10] 孔维刚，李文婧，王秋艳，等.基于改进YOLOv4算法的轻量化网络设计与实现[J].计算机工程，2022，48（3）：181-188.
KONG W G，LI W J，WANG Q Y，et al.Design and implementation of lightweight network based on improved YOLOv4 algorithm[J].Computer Engineering，2022，48（3）：181-188.
[11] 王长清，贺坤宇，蒋帅.改进YOLOv4-tiny网络的狭小空间目标检测方法[J].计算机工程与应用，2022，58（10）：240-248.
WANG C Q，HE K Y，JIANG S.Narrow space object detection method by improved YOLOv4-tiny network[J].Computer Engineering and Applications，2022，58（10）：240-248.
[12] 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[J].计算机工程与应用，2020，56（12）：1-7.
NIU Z D，TAN T，LI H D，et al.Improved RetinaFace’s natural scene mask wearing detection algorithm[J].Computer Engineering and Applications，2020，56（12）：1-7.
[13] 邓黄潇.基于迁移学习与RetinaNet的口罩佩戴检测的方法[J].电子技术与软件工程，2020（5）：209-211.
DENG H X.Method of mask wearing detection based on ransfer learning and RetinaNet[J].Electronic Technology and Software Engineering，2020（5）：209-211.
[14] 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[J].计算机工程，2020，46（11）：12-22.
WANG Y H，DING H W，LI B，et al.Mask wearing detection algorithm based on improved YOLOv3 in complex scenes[J].Computer Engineering，2020，46（11）：12-22.
[15] JIANG Z C，ZHAO L Q，LI S Y，et al.Real-time object detection method based on improved YOLOv4-tiny[J].arXiv：2011.04244，2020.
[16] HOU Q，ZHOU D，FENG J.Coordinate attention for efficient mobile network design[J].arXiv：2103.02907，2021.
[17] YU F，CHEN H F，WANG X，et al.BDD100K：a diverse driving dataset for heterogeneous multitask learning[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition，Seattle，Jun 13-19，2020.Piscataway：IEEE，2020：2633-2642.
[18] REZATOFIGHI H，TSOI N，GWAK J Y，et al.Generalized intersection over union：a metric and a loss for bounding box regression[J].arXiv：1902.09630，2019.
[19] HE K M，ZHANG X Y，REN S Q，et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2015，37（9）：1904-1916.
[20] WOO S，PARK J，LEE J Y，et al.CBAM：convolutional block attention module[J].arXiv：1807.06521，2018.
[21] LIU J，ZHANG D.Research on vehicle object detection algorithm based on improved YOLOv3 algorithm[J].Proceedings of the Journal of Physics：Conference Series，2020，1575（1）：012150.
[22] 王兵，乐红霞，李文璟，等.改进YOLO轻量化网络的口罩检测算法[J].计算机工程与应用，2021，57（8）：62-69.
WANG B，LE H X，LI W J，et al.Mask detection algorithm based on improved YOLO lightweight network[J].Computer Engineering and Applications，2021，57（8）：62-69.
[23] LIN T Y，GOYAL P，GIRSHICK R，et al.Focal loss for dense object detection[J].IEEE Trans Pattern Anal Mach Intell，2020，42（2）：318-327.
[24] 钱伍，王国中，李国平.改进YOLOv5的交通实时检测鲁棒算法[J].计算机科学与探索，2022，16（1）：231-241.
QIAN W，WANG G Z，LI G P.Improved YOLOv5 traffic light real-time detection robust algorithm[J].Journal of Frontiers of Computer Science and Technology，2022，16（1）：231-241.
[25] 朱杰，王建立，王斌.基于YOLOv4-tiny改进的轻量级口罩检测算法[J].液晶与显示，2021，36（11）：1525-1534.
ZHU J，WANG J L，WANG B.Lightweight mask detection algorithm based on improved YOLOv4-tiny[J].Chinese Journal of Liquid Crystals and Displays，2021，36（11）：1525-1534.