FACE MASK DETECTION ALERT SYSTEM

2021 ◽  
Vol 1 (10) ◽  
pp. 364-368
Author(s):  
Samrat Bhardwaj ◽  
Neha Agrawal ◽  
M L Sharma
Keyword(s):  
Large Scale ◽  
Learning Algorithm ◽  
Face Mask ◽  
Video Stream ◽  
Alert System ◽  
Public Places ◽  
Railway Stations ◽  
Deep Learning Algorithm ◽  
Safety Improvement ◽  
Safety Guidelines

In the present scenario due to Covid-19, there are no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential districts, large-scale manufacturers and other enterprises to ensure safety. This system can therefore be used in real-time applications which require face-mask detection for safety purposes due to the outbreak of Covid-19. This project can be integrated with embedded systems for application in airports, railway stations, offices, schools, and public places to ensure that public safety guidelines are followed. To identify the person on image/video stream wearing face mask or not. If the person doesn’t wear a mask, the notification will be sent to the respected admin with the help of Python and deep learning algorithm by using the Convolutional Neural Network, Keras Framework and OpenCV. Keywords: Computer Vision, Object Detection, Object Tracking, COVID-19, Face Masks, Safety Improvement

scholarly journals Face Mask Detection System using Deep Learning

2020 ◽  
Vol 6 (12) ◽  
pp. 161-164
Author(s):  
Pinki and Prof. Sachin Garg
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Detection System ◽  
Face Mask ◽  
Video Stream ◽  
Public Places ◽  
Railway Stations ◽  
Deep Learning Algorithm ◽  
Safety Guidelines

In the present scenario due to Covid-19, there is no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential districts, large-scale manufacturers and other enterprises to ensure safety. This system can therefore be used in real-time applications which require face-mask detection for safety purposes due to the outbreak of Covid-19. This project can be integrated with embedded systems for application in airports, railway stations, offices, schools, and public places to ensure that public safety guidelines are followed. To identify the person on image/video stream wearing face mask or not. If the person doesn’t wear a mask, the notification will be sent to the respected admin with the help of Python and deep learning algorithm by using the Convolutional Neural Network, Keras Framework and OpenCV.

scholarly journals Comparative Analysis of Image Classification Algorithms for Face Mask Detection

2021 ◽  
Vol 7 (1) ◽  
pp. 56
Author(s):  
Mohammad Farid Naufal ◽  
Selvia Ferdiana Kusuma ◽  
Zefanya Ardya Prayuska ◽  
Ang Alexander Yoshua ◽  
Yohanes Albert Lauwoto ◽  
...  
Keyword(s):  
Image Classification ◽  
Learning Algorithm ◽  
Image Data ◽  
Face Mask ◽  
Classification Model ◽  
Support Vector ◽  
Classification Algorithms ◽  
Alert System ◽  
K Nearest Neighbors ◽  
Deep Learning Algorithm

Background: The COVID-19 pandemic remains a problem in 2021. Health protocols are needed to prevent the spread, including wearing a face mask. Enforcing people to wear face masks is tiring. AI can be used to classify images for face mask detection. There are a lot of image classification algorithm for face mask detection, but there are still no studies that compare their performance.Objective: This study aims to compare the classification algorithms of classical machine learning. They are k-nearest neighbors (KNN), support vector machine (SVM), and a widely used deep learning algorithm for image classification which is convolutional neural network (CNN) for face masks detection.Methods: This study uses 5 and 3 cross-validation for assessing the performance of KNN, SVM, and CNN in face mask detection.Results: CNN has the best average performance with the accuracy of 0.9683 and average execution time of 2,507.802 seconds for classifying 3,725 faces with mask and 3,828 faces without mask images.Conclusion: For a large amount of image data, KNN and SVM can be used as temporary algorithms in face mask detection due to their faster execution times. At the same time, CNN can be trained to form a classification model. In this case, it is advisable to use CNN for classification because it has better performance than KNN and SVM. In the future, the classification model can be implemented for automatic alert system to detect and warn people who are not wearing face masks.  

Monitoring the Movements of Wild Animals and Alert System using Deep Learning Algorithm

2021 ◽  
Author(s):  
Y A Roopashree ◽  
M Bhoomika ◽  
R Priyanka ◽  
K Nisarga ◽  
Sagarika Behera
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Wild Animals ◽  
Alert System ◽  
Deep Learning Algorithm

scholarly journals Research on Efficient Deep Learning Algorithm Based on ShuffleGhost in the Field of Virtual Reality

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bangtong Huang ◽  
Hongquan Zhang ◽  
Zihong Chen ◽  
Lingling Li ◽  
Lihua Shi
Keyword(s):  
Virtual Reality ◽  
Deep Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Feature Maps ◽  
Embedded Devices ◽  
Feature Map ◽  
Deep Learning Algorithm ◽  
Proper Design ◽  
The Cost

Deep learning algorithms are facing the limitation in virtual reality application due to the cost of memory, computation, and real-time computation problem. Models with rigorous performance might suffer from enormous parameters and large-scale structure, and it would be hard to replant them onto embedded devices. In this paper, with the inspiration of GhostNet, we proposed an efficient structure ShuffleGhost to make use of the redundancy in feature maps to alleviate the cost of computations, as well as tackling some drawbacks of GhostNet. Since GhostNet suffers from high computation of convolution in Ghost module and shortcut, the restriction of downsampling would make it more difficult to apply Ghost module and Ghost bottleneck to other backbone. This paper proposes three new kinds of ShuffleGhost structure to tackle the drawbacks of GhostNet. The ShuffleGhost module and ShuffleGhost bottlenecks are utilized by the shuffle layer and group convolution from ShuffleNet, and they are designed to redistribute the feature maps concatenated from Ghost Feature Map and Primary Feature Map. Besides, they eliminate the gap of them and extract the features. Then, SENet layer is adopted to reduce the computation cost of group convolution, as well as evaluating the importance of the feature maps which concatenated from Ghost Feature Maps and Primary Feature Maps and giving proper weights for the feature maps. This paper conducted some experiments and proved that the ShuffleGhostV3 has smaller trainable parameters and FLOPs with the ensurance of accuracy. And with proper design, it could be more efficient in both GPU and CPU side.

scholarly journals A deep learning view of the census of galaxy clusters in IllustrisTNG

2020 ◽  
Vol 498 (4) ◽  
pp. 5620-5628
Author(s):  
Y Su ◽  
Y Zhang ◽  
G Liang ◽  
J A ZuHone ◽  
D J Barnes ◽  
...  
Keyword(s):  
Deep Learning ◽  
Galaxy Clusters ◽  
Large Scale ◽  
Learning Algorithm ◽  
X Ray ◽  
Deep Learning Algorithm ◽  
Cluster Evolution ◽  
Novel Method ◽  
Massive Clusters ◽  
Activation Mapping

ABSTRACT The origin of the diverse population of galaxy clusters remains an unexplained aspect of large-scale structure formation and cluster evolution. We present a novel method of using X-ray images to identify cool core (CC), weak cool core (WCC), and non-cool core (NCC) clusters of galaxies that are defined by their central cooling times. We employ a convolutional neural network, ResNet-18, which is commonly used for image analysis, to classify clusters. We produce mock Chandra X-ray observations for a sample of 318 massive clusters drawn from the IllustrisTNG simulations. The network is trained and tested with low-resolution mock Chandra images covering a central 1 Mpc square for the clusters in our sample. Without any spectral information, the deep learning algorithm is able to identify CC, WCC, and NCC clusters, achieving balanced accuracies (BAcc) of 92 per cent, 81 per cent, and 83 per cent, respectively. The performance is superior to classification by conventional methods using central gas densities, with an average ${\rm BAcc}=81{{\ \rm per\ cent}}$, or surface brightness concentrations, giving ${\rm BAcc}=73{{\ \rm per\ cent}}$. We use class activation mapping to localize discriminative regions for the classification decision. From this analysis, we observe that the network has utilized regions from cluster centres out to r ≈ 300 kpc and r ≈ 500 kpc to identify CC and NCC clusters, respectively. It may have recognized features in the intracluster medium that are associated with AGN feedback and disruptive major mergers.

scholarly journals LEAF DISEASE DETECTION OF CUCURBITS USING CNN

2021 ◽  
Vol 40 ◽  
pp. 03043
Author(s):  
Isha Agrawal ◽  
Prada Hegde ◽  
Pooja Shetty ◽  
Priyanka Shingane
Keyword(s):  
Large Scale ◽  
Plant Disease ◽  
Learning Algorithm ◽  
Early Stage ◽  
Economic Value ◽  
Disease Detection ◽  
Knowledge Processing ◽  
Leaf Disease ◽  
Deep Learning Algorithm ◽  
Work Knowledge

Identification of plant disease is tough in agribusiness arena. If it is inaccurate, there occurs a tremendous damage in the production and economical price. Leaf Disease detection requires huge amount of work, knowledge, processing time in plant disease. The most used and edible vegetable all over the world is from cucurbitaceous family. The crops under this family have great economic value in the food industry and its production is done in large scale. This family consists of 965 species. If any of these plants catch disease then there would be a tremendous loss in the production of this field yields. Thus, treating them at early stage is best way to prevent such losses. Hence, Deep Learning Algorithm like CNN can be used to detect the diseases of the plants. The leaves of the plants would be used as primary material for identification of the disease, as they are much more visible on the leaves.

scholarly journals Face Mask Detection

2021 ◽  
Vol 10 (3) ◽  
pp. 1520-1523
Keyword(s):  
Deep Learning ◽  
Face Mask ◽  
Video Stream ◽  
Still Images ◽  
Railway Stations ◽  
Health Authorities ◽  
Safety And Health ◽  
Learning Techniques ◽  
The Face ◽  
The Right

Businessesare constantly overhauling their existing infrastructure and processes to be more efficient, safe, and usable for employees, customers, and the community. With the ongoing pandemic, it's even more important to have advanced applications and services in place to mitigate risk. For public safety and health, authorities are recommending the use of face masks and coverings to control the spread of Coronovirus. The COVID-19 pandemic is devastation to themankind irrespective of caste, creed, gender, and religion. Using a face mask can undoubtedly help in managing the spread of the virus. COVID-19 face mask detector uses deep learning techniques to successfully test whether a person is wearing a face mask or not. Using a deep learning method called Convolutional Neural Network, got an accuracy of 98.6 %. It can work with still images and also works with a live video stream. Cases in which the mask is improperly worn are when the nose and mouth are partially covered is considered as the mask is not worn. Our face mask identifier is the least complex in structure and gives quick results and hence can be used in CCTV footage to detect whether a person is wearing a mask perfectly so that he does not pose any danger to others. Mass screening is possible and hence can be used in crowded places like railway stations, bus stops, markets, streets, mall entrances, schools, colleges, etc. By monitoring the placement of the face mask on the face, we can make sure that an individual wears it the right way and helps to curb the scope of the virus

scholarly journals Selective Disinfection Based on Directional Ultraviolet Irradiation and Artificial Intelligence

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2557
Author(s):  
Ben Zierdt ◽  
Taichu Shi ◽  
Thomas DeGroat ◽  
Sam Furman ◽  
Nicholas Papas ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Ultraviolet Radiation ◽  
Ultraviolet Irradiation ◽  
Human Exposure ◽  
Large Scale ◽  
Learning Algorithm ◽  
Ultraviolet Disinfection ◽  
Time Efficiency ◽  
Deep Learning Algorithm

Ultraviolet disinfection has been proven to be effective for surface sanitation. Traditional ultraviolet disinfection systems generate omnidirectional radiation, which introduces safety concerns regarding human exposure. Large scale disinfection must be performed without humans present, which limits the time efficiency of disinfection. We propose and experimentally demonstrate a targeted ultraviolet disinfection system using a combination of robotics, lasers, and deep learning. The system uses a laser-galvo and a camera mounted on a two-axis gimbal running a custom deep learning algorithm. This allows ultraviolet radiation to be applied to any surface in the room where it is mounted, and the algorithm ensures that the laser targets the desired surfaces avoids others such as humans. Both the laser-galvo and the deep learning algorithm were tested for targeted disinfection.

scholarly journals Artificial intelligence utilising corneal confocal microscopy for the diagnosis of peripheral neuropathy in diabetes mellitus and prediabetes

Diabetologia ◽  
2021 ◽  
Author(s):  
Frank G. Preston ◽  
Yanda Meng ◽  
Jamie Burgess ◽  
Maryam Ferdousi ◽  
Shazli Azmi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Peripheral Neuropathy ◽  
Confocal Microscopy ◽  
Large Scale ◽  
Data Augmentation ◽  
Learning Algorithm ◽  
Deep Learning Algorithm ◽  
Corneal Confocal Microscopy ◽  
Corneal Nerves ◽  
Corneal Nerve

Abstract Aims/hypothesis We aimed to develop an artificial intelligence (AI)-based deep learning algorithm (DLA) applying attribution methods without image segmentation to corneal confocal microscopy images and to accurately classify peripheral neuropathy (or lack of). Methods The AI-based DLA utilised convolutional neural networks with data augmentation to increase the algorithm’s generalisability. The algorithm was trained using a high-end graphics processor for 300 epochs on 329 corneal nerve images and tested on 40 images (1 image/participant). Participants consisted of healthy volunteer (HV) participants (n = 90) and participants with type 1 diabetes (n = 88), type 2 diabetes (n = 141) and prediabetes (n = 50) (defined as impaired fasting glucose, impaired glucose tolerance or a combination of both), and were classified into HV, those without neuropathy (PN−) (n = 149) and those with neuropathy (PN+) (n = 130). For the AI-based DLA, a modified residual neural network called ResNet-50 was developed and used to extract features from images and perform classification. The algorithm was tested on 40 participants (15 HV, 13 PN−, 12 PN+). Attribution methods gradient-weighted class activation mapping (Grad-CAM), Guided Grad-CAM and occlusion sensitivity displayed the areas within the image that had the greatest impact on the decision of the algorithm. Results The results were as follows: HV: recall of 1.0 (95% CI 1.0, 1.0), precision of 0.83 (95% CI 0.65, 1.0), F1-score of 0.91 (95% CI 0.79, 1.0); PN−: recall of 0.85 (95% CI 0.62, 1.0), precision of 0.92 (95% CI 0.73, 1.0), F1-score of 0.88 (95% CI 0.71, 1.0); PN+: recall of 0.83 (95% CI 0.58, 1.0), precision of 1.0 (95% CI 1.0, 1.0), F1-score of 0.91 (95% CI 0.74, 1.0). The features displayed by the attribution methods demonstrated more corneal nerves in HV, a reduction in corneal nerves for PN− and an absence of corneal nerves for PN+ images. Conclusions/interpretation We demonstrate promising results in the rapid classification of peripheral neuropathy using a single corneal image. A large-scale multicentre validation study is required to assess the utility of AI-based DLA in screening and diagnostic programmes for diabetic neuropathy. Graphical abstract

DETECTION OF FACE MASK USING DEEP LEARNING

2021 ◽  
Vol 1 (9) ◽  
pp. 161-168
Author(s):  
Manak Bansal ◽  
Manaswi Manaswi ◽  
Pankaj Kumar ◽  
Jyoti Kaushik
Keyword(s):  
Infected Individual ◽  
Face Mask ◽  
Practical Application ◽  
Public Places ◽  
Railway Stations ◽  
The Great Depression ◽  
Global Recession ◽  
Specificity And Sensitivity ◽  
Area Unit ◽  
Severe Health Problem

As we know, the COVID-19 pandemic additionally referred to as the coronavirus pandemic, is an ongoing pandemic of coronavirus sickness since 2019. This infectious disease was first detected in Wuhan, China in late 2019. Symptoms of COVID-19 are highly variable, ranging from none to severe health problem. The virus spreads chiefly through the air when individuals are close to one another. It transmits from an infected individual through the droplets as they breathe, cough, sneeze or speak and these droplets then enters another individual via their mouth, nose, or eyes. It might also spread via contaminated surfaces. Individuals stay infectious for up to 2 weeks and can spread the virus albeit they do not have symptoms. As of 1st November 2021, more than 200 million cases are confirmed, with more than 500 million deaths due to COVID-19. The pandemic has been the reason for global social and economic disruption, including the largest global recession since the Great Depression. The recommended preventive measures include social distancing, carrying a mask publicly, ventilation and air-filtering, hand washing, covering one‟s mouth when sneezing or coughing and self-isolation for individuals exposed. In present endeavour therefore, the author has attempted to make one thing associated with it, that's deciding whether a dividual is carrying a mask or not. The complete investigations area unit distributed in various chapters that embody the current thesis. The performance of our model will be evaluated in precision, accuracy, recall, specificity, and sensitivity that demonstrate the practical application of this model. The system performs with an accuracy of 99.88%, precision of 99.49%, sensitivity of 99.77%, and specificity of 99.6. Thus, this model tracks if people are using masks or not in real-time using a device camera. This model can be used with the current camera infrastructure to enable this tool which can be used in various public places such as markets or railway stations or offices etc. Keywords: Covid-19, Face Mask Detection, Convolutional Neural Network, MobileNetV2, Precaution.

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