<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In recent years, COVID-19 has swept the world, and people in crowded public places are usually large. In order to reduce the risk of virus transmission, stop the spread of the epidemic and reduce cross-infection, wearing masks correctly has become an important measure to prevent the virus. Aiming at the time-consuming and laborious situation of wearing masks manually, this paper proposes a mask wearing detection method based on yolov5. The input layer is mainly used for mosaic data enhancement, that is, adaptive anchor box and adaptive image scaling technology; Yolov5 in backbone mainly adopts focus and CSP (cross stage partial) structure; The neck layer adopts spp (spatial pyramid pooling) module and FPN (feature pyramid networks) + pan (pixel aggregation network) structure; The output mainly adopts ciou for the bounding box loss function_Loss is the average index of NMS (non maximum suppression). This method uses 8000 preprocessed images as the data set and trains 200 epochs to get the final model. The algorithm visually displays the training and test results through tensor board, and inputs the pictures captured by the camera into the model to detect whether the face wears a mask. The accuracy, recall and mean accuracy (map) of the algorithm on the test set are 94.8%, 89.0% and 93.5% respectively, which are higher than the detection results of yolov3 and yolov4 algorithms.</p>
</abstract>

In recent years, COVID-19 has swept the world, and people in crowded public places are usually large. In order to reduce the risk of virus transmission, stop the spread of the epidemic and reduce cross-infection, wearing masks correctly has become an important measure to prevent the virus. Aiming at the time-consuming and laborious situation of wearing masks manually, this paper proposes a mask wearing detection method based on yolov5. The input layer is mainly used for mosaic data enhancement, that is, adaptive anchor box and adaptive image scaling technology; Yolov5 in backbone mainly adopts focus and CSP (cross stage partial) structure; The neck layer adopts spp (spatial pyramid pooling) module and FPN (feature pyramid networks) + pan (pixel aggregation network) structure; The output mainly adopts ciou for the bounding box loss function_Loss is the average index of NMS (non maximum suppression). This method uses 8000 preprocessed images as the data set and trains 200 epochs to get the final model. The algorithm visually displays the training and test results through tensor board, and inputs the pictures captured by the camera into the model to detect whether the face wears a mask. The accuracy, recall and mean accuracy (map) of the algorithm on the test set are 94.8%, 89.0% and 93.5% respectively, which are higher than the detection results of yolov3 and yolov4 algorithms.

Face Mask Wearing Detection Based on YOLOv5

School of Computer Science and Engineering

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Face Mask Wearing Detection Based on YOLOv5"}

In recent years, COVID-19 has swept the world, and people in crowded public places are usually large. In order to reduce the risk of virus transmission, stop...

Face Mask Wearing Detection Based on YOLOv5

Pubblicato online: 26 mag 2023

Pagine: 67 - 75

DOI: https://doi.org/10.2478/ijanmc-2022-0017

Parole chiave
Mask Detection, Yolov5, CIOU Loss Function

© 2022 Yunshan Xie et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Face Mask Wearing Detection Based on YOLOv5

Pubblicato online: 26 mag 2023

Pagine: 67 - 75

DOI: https://doi.org/10.2478/ijanmc-2022-0017

Parole chiaveMask Detection, Yolov5, CIOU Loss Function

© 2022 Yunshan Xie et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Mask Detection, Yolov5, CIOU Loss Function