改进YOLO的口罩佩戴实时检测方法

doi:10.16180/j.cnki.issn1007-7820.2023.02.011

Abstract

Abstract:

The existing YOLO target detection model is based on the One-stage idea for multi-target detection. It is insufficient for dual-classification detection, and the performance consumption is large during detection. In order to improve the detection efficiency of dual-classification mask wearing during the period of the outbreak of COVID-19, this study proposes a real-time detection method based on YOLO for detecting the condition of bi-objective mask wearing. The feedforward input layer of the model is improved, the data enhancement part is optimized, and adaptive image scaling is added to improve the detection accuracy and detection efficiency of dual-classification and small targets. The adaptive anchoring frame is added to replace the activation function so as to reduce the computational complexity of the method and improves the detection efficiency of the method. The optimization of Neck and the addition of Focus structure improve the capability of feature fusion and reduce the amount of parameters to raise the efficiency. The experimental results showed that compared with the YOLOv4, the proposed method has a 0.33% increase in F1 and a 0.71% increase in mAp in the data set in the text, and the detection efficiency is also significantly improved under the same experimental environment.

Key words: YOLOv4, CSPDenseNet, Focus, data augmentation, activation function, CSP2, target detection, mask wearing

CLC Number:

TP391

CHENG Changwen,CHEN Wei,CHEN Jinhong,YIN Zhong. YOLO-Improve Detection Method of Real-Time Mask Wearing[J].Electronic Science and Technology, 2023, 36(2): 73-80.

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References 26

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参数名	参数值	参数解释
hsv_h	0.015	图像 HSV-Hue 增强(小数)
hsv_s	0.7	图像HSV-饱和度增强(小数)
hsv_v	0.6	图像HSV-值增强(小数)
degrees	1.0	图像旋转(+/- deg)
translate	0.1	图像翻译(+/- fraction)
scale	0.6	图像比例(+/- gain)
shear	1.0	图像剪切(+/- deg)
perspective	0.0	图像透视(+/- fraction), range 0-0.001
flipud	0.01	图像上下翻转(比例)
fliplr	0.5	图像左右翻转(比例)
mixup	0.2	图像混合(比例)

实验环境	环境配置
操作系统	Windows10
处理器	i7-9750H
内存	32 GB
显卡	GTX-1080TI(11 GB)
开发环境	Python
编辑器	PyCharm
深度学习框架	Pytorch(v1.7)

实验参数	参数值
迭代次数(epochs)	300
每次处理张数	16
图像尺寸	608×608
学习率(lr)	0.01
学习率衰减(weight_decay)	5e^-4
是否开启余弦退火	false
是否开启马赛克增强	true
是否使用cuda	true

	正类	负类
正确	True Positive(TP)	True Negative(TN)
错误	False Positive(FP)	False Negative(FN)

方法	RetinaFace	DFS	RetinaNet
数据集	3 000	32 203	7 959
F₁分数/%	91.71	92.34	89.55
帧速率/frame·s^-1	18.30	16.80	20.82
各类别AP的平均值(mAP)/%	90.30	91.20	86.45

YOLO-Improve Detection Method of Real-Time Mask Wearing

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Figures/Tables 19

References 26

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对比方法	YOLOv4	YOLOv4-tiny	改进YOLO
One-stage	√	√	√
激活函数	Mish	Mish	SiLU
召回率/%	96.46	92.17	97.18
查准率/%	96.95	91.25	97.62
F₁分数/%	97.02	92.41	97.35
各类别AP的平均值/%	95.12	88.38	96.83
帧速率/frame·s^-1	13.02	43.73	30.82

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