Human Activities Detection using DeepLearning Technique- YOLOv8

Using a mask during the pandemic has occasionally been crucial and difficult. The use of universal masks can greatly lower and possibly even stop the spread of viruses within communities. So, mask detection has become a very critical task for security agencies in all the buildings, Government office...

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Bibliographic Details
Published in:ITM Web of Conferences 2023, Vol.56, p.3003
Main Authors: Motwani, Nilesh Parmanand, S, Soumya
Format: Article
Language:English
Subjects:
Artificial neural networks
Datasets
deep learning
Face recognition
human activity
Human activity recognition
Image processing
intersection over union
iou
object recognition
yolov8
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Summary:Using a mask during the pandemic has occasionally been crucial and difficult. The use of universal masks can greatly lower and possibly even stop the spread of viruses within communities. So, mask detection has become a very critical task for security agencies in all the buildings, Government offices & other places. With the advent of GPUs, high computing machines, and Deep Convolution Neural Networks (DCCN), automatic Face & Mask Detection is possible by considering the image processing feature of extracting, 3-dimensional shapes from 2- dimensional images. This paper discuss about the YOLOv8 model to confirm its overall applicability, on two datasets namely FDDB & MASK. This helps to examine the behavior of the feature from the Mask dataset, which is intended for COVID-19 Mask Detection alone. Mask is the main dataset in this experiment. Above this, the ImageNet dataset is utilized for pretraining and FDDB (Face Detection Dataset & Benchmarks) datasets for recognizing face of a human being. The precision of models on FDDB is 58.9 % & on MASK dataset is 66.5%.
ISSN:2271-2097
2431-7578
2271-2097
DOI:10.1051/itmconf/20235603003