SAI-YOLO: A Lightweight Network for Real-Time Detection of Driver Mask-Wearing Specification on Resource-Constrained Devices

Frequent occurrence and long-term existence of respiratory diseases such as COVID-19 and influenza require bus drivers to wear masks correctly during driving. To quickly detect whether the mask is worn correctly on resource-constrained devices, a lightweight target detection network SAI-YOLO is prop...

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Autores principales:	Zuopeng Zhao, Kai Hao, Xiaoping Ma, Xiaofeng Liu, Tianci Zheng, Junjie Xu, Shuya Cui
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/541151bfb3e44ccb8f802b1280dc3b74
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spelling	oai:doaj.org-article:541151bfb3e44ccb8f802b1280dc3b742021-11-22T01:10:50ZSAI-YOLO: A Lightweight Network for Real-Time Detection of Driver Mask-Wearing Specification on Resource-Constrained Devices1687-527310.1155/2021/4529107https://doaj.org/article/541151bfb3e44ccb8f802b1280dc3b742021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4529107https://doaj.org/toc/1687-5273Frequent occurrence and long-term existence of respiratory diseases such as COVID-19 and influenza require bus drivers to wear masks correctly during driving. To quickly detect whether the mask is worn correctly on resource-constrained devices, a lightweight target detection network SAI-YOLO is proposed. Based on YOLOv4-Tiny, the network incorporates the Inception V3 structure, replaces two CSPBlock modules with the RES-SEBlock modules to reduce the number of parameters and computational difficulty, and adds a convolutional block attention module and a squeeze-and-excitation module to extract key feature information. Moreover, a modified ReLU (M-ReLU) activation function is introduced to replace the original Leaky_ReLU function. The experimental results show that SAI-YOLO reduces the number of network parameters and calculation difficulty and improves the detection speed of the network while maintaining certain recognition accuracy. The mean average precision (mAP) for face-mask-wearing detection reaches 86% and the average precision (AP) for mask-wearing normative detection reaches 88%. In the resource-constrained device Raspberry Pi 4B, the average detection time after acceleration is 197 ms, which meets the actual application requirements.Zuopeng ZhaoKai HaoXiaoping MaXiaofeng LiuTianci ZhengJunjie XuShuya CuiHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Zuopeng Zhao Kai Hao Xiaoping Ma Xiaofeng Liu Tianci Zheng Junjie Xu Shuya Cui SAI-YOLO: A Lightweight Network for Real-Time Detection of Driver Mask-Wearing Specification on Resource-Constrained Devices
description	Frequent occurrence and long-term existence of respiratory diseases such as COVID-19 and influenza require bus drivers to wear masks correctly during driving. To quickly detect whether the mask is worn correctly on resource-constrained devices, a lightweight target detection network SAI-YOLO is proposed. Based on YOLOv4-Tiny, the network incorporates the Inception V3 structure, replaces two CSPBlock modules with the RES-SEBlock modules to reduce the number of parameters and computational difficulty, and adds a convolutional block attention module and a squeeze-and-excitation module to extract key feature information. Moreover, a modified ReLU (M-ReLU) activation function is introduced to replace the original Leaky_ReLU function. The experimental results show that SAI-YOLO reduces the number of network parameters and calculation difficulty and improves the detection speed of the network while maintaining certain recognition accuracy. The mean average precision (mAP) for face-mask-wearing detection reaches 86% and the average precision (AP) for mask-wearing normative detection reaches 88%. In the resource-constrained device Raspberry Pi 4B, the average detection time after acceleration is 197 ms, which meets the actual application requirements.
format	article
author	Zuopeng Zhao Kai Hao Xiaoping Ma Xiaofeng Liu Tianci Zheng Junjie Xu Shuya Cui
author_facet	Zuopeng Zhao Kai Hao Xiaoping Ma Xiaofeng Liu Tianci Zheng Junjie Xu Shuya Cui
author_sort	Zuopeng Zhao
title	SAI-YOLO: A Lightweight Network for Real-Time Detection of Driver Mask-Wearing Specification on Resource-Constrained Devices
title_short	SAI-YOLO: A Lightweight Network for Real-Time Detection of Driver Mask-Wearing Specification on Resource-Constrained Devices
title_full	SAI-YOLO: A Lightweight Network for Real-Time Detection of Driver Mask-Wearing Specification on Resource-Constrained Devices
title_fullStr	SAI-YOLO: A Lightweight Network for Real-Time Detection of Driver Mask-Wearing Specification on Resource-Constrained Devices
title_full_unstemmed	SAI-YOLO: A Lightweight Network for Real-Time Detection of Driver Mask-Wearing Specification on Resource-Constrained Devices
title_sort	sai-yolo: a lightweight network for real-time detection of driver mask-wearing specification on resource-constrained devices
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/541151bfb3e44ccb8f802b1280dc3b74
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SAI-YOLO: A Lightweight Network for Real-Time Detection of Driver Mask-Wearing Specification on Resource-Constrained Devices

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