Drone image based illegal crowddetection for COVID-19 disease prevention via convolutional neural networks (CNNS)transfer learning
COVID-19 originated in Wuhan, China, in December 2019 and quickly became a global outbreak in January 2020. COVID-19 is a disease caused by SARS-CoV-2, which is a human transmission disease. Since it is a human transmission disease, thus mass gathering in public is not allowed to prevent the possibl...
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Online Access: | http://eprints.utm.my/104063/1/OngJiaEekMuhammadAmirAsari2022_DroneImageBasedIllegalCrowd.pdf http://eprints.utm.my/104063/ http://dx.doi.org/10.11113/humentech.v1n2.20 |
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my.utm.1040632024-01-14T00:59:43Z http://eprints.utm.my/104063/ Drone image based illegal crowddetection for COVID-19 disease prevention via convolutional neural networks (CNNS)transfer learning As Ari, Muhammad Amir Ong, Jia Eek T Technology (General) TJ Mechanical engineering and machinery COVID-19 originated in Wuhan, China, in December 2019 and quickly became a global outbreak in January 2020. COVID-19 is a disease caused by SARS-CoV-2, which is a human transmission disease. Since it is a human transmission disease, thus mass gathering in public is not allowed to prevent the possible spread of COVID-19. However, the current monitoring technology, such as closed-circuit television (CCTV), only cover a limited area of the public and lack of mobility. Image classification is one of the approaches that can detect crowds in an image and can be done through either machine learning or deep learning approach. Recently, deep learning, especially convolutional neural networks (CNNs) outperform classical machine learning in image classification and the common approach for modelling CNN is through transfer learning. Thus, this study aims to develop a convolutional neural network that can detect illegal crowd gathering from offline drone view images through image classification using the transfer learning technique. Several models are used to train on the same dataset obtained, and the all-model performance is evaluated through a confusion matrix. Based on performance analysis, it shows that the ResNet50 model outperforms the VGG16 model and InceptionV3 model by achieving 95% test accuracy, 95% precision, 95% recall and 95% F1-score. In conclusion, it can be concluded that the deep learning approach uses a pre-trained convolutional neural network that can be used to classify object images in this study. Penerbit UTM Press 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/104063/1/OngJiaEekMuhammadAmirAsari2022_DroneImageBasedIllegalCrowd.pdf As Ari, Muhammad Amir and Ong, Jia Eek (2022) Drone image based illegal crowddetection for COVID-19 disease prevention via convolutional neural networks (CNNS)transfer learning. Journal of Human Centered Technology, 1 (2). pp. 7-16. ISSN 2821-2467 http://dx.doi.org/10.11113/humentech.v1n2.20 DOI:10.11113/humentech.v1n2.20 |
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T Technology (General) TJ Mechanical engineering and machinery As Ari, Muhammad Amir Ong, Jia Eek Drone image based illegal crowddetection for COVID-19 disease prevention via convolutional neural networks (CNNS)transfer learning |
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COVID-19 originated in Wuhan, China, in December 2019 and quickly became a global outbreak in January 2020. COVID-19 is a disease caused by SARS-CoV-2, which is a human transmission disease. Since it is a human transmission disease, thus mass gathering in public is not allowed to prevent the possible spread of COVID-19. However, the current monitoring technology, such as closed-circuit television (CCTV), only cover a limited area of the public and lack of mobility. Image classification is one of the approaches that can detect crowds in an image and can be done through either machine learning or deep learning approach. Recently, deep learning, especially convolutional neural networks (CNNs) outperform classical machine learning in image classification and the common approach for modelling CNN is through transfer learning. Thus, this study aims to develop a convolutional neural network that can detect illegal crowd gathering from offline drone view images through image classification using the transfer learning technique. Several models are used to train on the same dataset obtained, and the all-model performance is evaluated through a confusion matrix. Based on performance analysis, it shows that the ResNet50 model outperforms the VGG16 model and InceptionV3 model by achieving 95% test accuracy, 95% precision, 95% recall and 95% F1-score. In conclusion, it can be concluded that the deep learning approach uses a pre-trained convolutional neural network that can be used to classify object images in this study. |
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Article |
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As Ari, Muhammad Amir Ong, Jia Eek |
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As Ari, Muhammad Amir Ong, Jia Eek |
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As Ari, Muhammad Amir |
title |
Drone image based illegal crowddetection for COVID-19 disease prevention via convolutional neural networks (CNNS)transfer learning |
title_short |
Drone image based illegal crowddetection for COVID-19 disease prevention via convolutional neural networks (CNNS)transfer learning |
title_full |
Drone image based illegal crowddetection for COVID-19 disease prevention via convolutional neural networks (CNNS)transfer learning |
title_fullStr |
Drone image based illegal crowddetection for COVID-19 disease prevention via convolutional neural networks (CNNS)transfer learning |
title_full_unstemmed |
Drone image based illegal crowddetection for COVID-19 disease prevention via convolutional neural networks (CNNS)transfer learning |
title_sort |
drone image based illegal crowddetection for covid-19 disease prevention via convolutional neural networks (cnns)transfer learning |
publisher |
Penerbit UTM Press |
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2022 |
url |
http://eprints.utm.my/104063/1/OngJiaEekMuhammadAmirAsari2022_DroneImageBasedIllegalCrowd.pdf http://eprints.utm.my/104063/ http://dx.doi.org/10.11113/humentech.v1n2.20 |
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