Facemask Wearing Detection Based on Deep CNN to Control COVID-19 Transmission

Waleed, Jumana; Abbas, Thekra; Hasan, Taha Mohammed

doi:10.1109/micest54286.2022.9790197

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…Classes Performance (Agarwal et al, 2022) 2 99,11% Accuracy (Goyal et al, 2022) 2 98% Accuracy (Waleed et al, 2022) 2 99,57% Accuracy 2 (Case 1) 97,49% Accuracy (Aydemir et al, 2022) 2 (Case 2) 100% Accuracy 3 (Case 3) 95,95% Accuracy (Guo et al, 2022) 3 96,7% mAP (Han et al, 2022) 3 67,01% mAP (Eyiokur et al, 2021) 3 98,20% Accuracy This study 3 99,6% Accuracy Source: Authors…”

Section: Referencementioning

confidence: 95%

Mask Detection and Categorization during the COVID-19 Pandemic Using Deep Convolutional Neural Network

Dimililer,

Kayali

2023

Ing. Inv.

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With COVID-19 spreading all over the world and restricting our daily lives, the use of face masks has become very important, as it is an efficient way of slowing down the spread of the virus and an important piece to continue our daily tasks until vaccination is completed. People have been fighting this disease for a long time, and they are bored with the precautions, so they act carelessly. In this case, automatic detection systems are very important to keep the situation under control. In this research, deep learning models are trained with as little input data as possible in order to obtain an accurate face mask-wearing condition classification. These classes are mask-correct, mask wrong, and no mask, which refers to proper face mask use, improper face mask use, and no mask use, respectively. DenseNets, EfficientNets, InceptionResNetV2, InceptionV3, MobileNets, NasNets, ResNets, VGG16, VGG19, and Xception are the networks used in this study. The highest accuracy was obtained by the InceptionResNetV2 and Xception networks, with 99,6%. When other performance parameters are taken into consideration, the Xception network is a step forward. VGG16 and VGG19 also show an accuracy rate over 99%, with 99,1 and 99,4%, respectively. These two networks also had higher FPS and the two lowest initialization times during implementation. A comparison with recent studies was also carried out to evaluate the obtained accuracy. It was found that a higher accuracy can be obtained with the possible minimum input size.

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Section: Referencementioning

confidence: 95%

Mask Detection and Categorization during the COVID-19 Pandemic Using Deep Convolutional Neural Network

Dimililer,

Kayali

2023

Ing. Inv.

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“…The commonly utilized metrics of evaluation (F1 metric (F), accuracy (Ac), precision (P), and recall (R)) are given as follows [45]:…”

Section: Performance Evaluation and Analysismentioning

confidence: 99%

Pedestrian Attributes and Activity Recognition Using Deep Learning: A Comprehensive Survey

2023

IJSER

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In recent years, pedestrian attributes and activity recognition has attracted expanding research emphasis due to their considerable research importance and value of application in the intelligent civil and military domains. Owing to the inadequate image or frame quality of inexpensive cameras and the absence of obvious and stable feature information, direction of pedestrian movement, and so on, the complication of pedestrian attributes and activity recognition is expanded. However, with the comprehensive implementation of deep learning techniques, pedestrian attributes and activity recognition has made a substantial advance. This paper is the first of its kind because it combines the recognition of pedestrian attributes and activities separately, and reviews the works on pedestrian attributes and activities using deep learning in relation to datasets. The fundamental concepts, corresponding challenges, and popular solutions are also explained. Furthermore, in this community, metrics of evaluation and concise performance comparisons are given. In the end, the hotspots of the present research and the directions of the future research are summarized.

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“…Afterward, these obtained features are weighted and merged into a fully connected layer (one or more layers), and this process indicates the phase of classification. Lastly, in the task of classification, the output layer holds one neuron for each category [6]. When the utilized activation function is a soft max, then the output of each neuron indicates the probability of the posterior category [7].…”

Section: Figure 1 the Typical Construction Of Cnnmentioning

confidence: 99%

An abbreviated review of deep learning-based image classification models

Abbood

Al-Turfi

Almajmaie

2023

IJEECS

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Image classification is an extensively researched sub-fields of computer vision implemented in face recognition, self-driving, medical image segmentation, biological identification, and others. Traditional models of image classification require manual construction of feature extraction techniques and classification accuracy which are closely associated with these utilized techniques. During the rapid progress of multimedia technologies, the number of images that require classification got bigger, and this led to making image classification more complicated, hence, the manual construction of feature extraction techniques consumes more time and provides lower accuracy. In the recent decade, deep learning-based models have appeared in various applications. These models hold the merits of an effective extraction of image features, low-weight features filtering, a large capacity for processing, and higher classification speed and accuracy. Thus, lots of researchers have attempted to utilize deep learning algorithms, especially convolutional neural networks (CNNs) for image classification. Therefore, this paper concentrates on providing an abbreviated review of deep learning-based image classification models, by covering the recently utilized deep learning algorithms, comparing various related works and benchmark datasets mentioned in this paper, and summarizing the fundamental analysis and discussion.

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Facemask Wearing Detection Based on Deep CNN to Control COVID-19 Transmission

Cited by 7 publications

References 13 publications

Mask Detection and Categorization during the COVID-19 Pandemic Using Deep Convolutional Neural Network

Mask Detection and Categorization during the COVID-19 Pandemic Using Deep Convolutional Neural Network

Pedestrian Attributes and Activity Recognition Using Deep Learning: A Comprehensive Survey

An abbreviated review of deep learning-based image classification models

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