A Mask Detection Algorithm Based on Improved Yolov5s

Aiming at the severe form of new coronavirus epidemic prevention and control, a target detection algorithm is proposed to detect whether masks are worn in public places. The Ghostnet and SElayer modules with fewer design parameters replace the BottleneckCSP part in the original Yolov5s network, which reduces the computational complexity of the model and improves the detection accuracy. The bounding box regression loss function DIOU is optimized, the DGIOU loss function is used for bounding box regression, and the center coordinate distance between the two bounding boxes is considered to achieve a better convergence effect. In the feature pyramid, the depthwise separable convolution DW is used to replace the ordinary convolution, which further reduces the amount of parameters and reduces the loss of feature information caused by multiple convolutions. The experimental results show that compared with the yolov5s algorithm, the proposed method improves the mAP by 4.6% and the detection rate by 10.7 frame/s in the mask wearing detection. Compared with other mainstream algorithms, the improved yolov5s algorithm has better generalization ability and practicability. © 2022 ACM.

Improved mask detection based on YOLOv5s

COVID-19 epidemic is not over. The correct wearing of masks can effectively prevent the spread of the virus. Aiming at a series of problems of existing mask-wearing detection algorithms, such as only detecting whether to wear or not, being unable to detect whether to wear correctly, difficulty in detecting small targets in dense scenes, and low detection accuracy, It is suggested to use a better algorithm based on YOLOv5s. It improves the generalization and transmission performance of the model by changing the ACON activation function. Then Bifpn is used to replace PAN to effectively integrate the target features of different sizes extracted by the network. Finally, To enable the network to pay attention to a wide area, CA is introduced to the backbone. This embeds the location information into the channel attention. © 2023 SPIE.

An Automatic CNN-based Face Mask Detection Algorithm Tested During the COVID-19 Pandemics

The ongoing COVID-19 pandemic has highlighted the importance of wearing face masks as a preventive measure to reduce the spread of the virus. In medical settings, such as hospitals and clinics, healthcare professionals and patients are required to wear surgical masks for infection control. However, the use of masks can hinder facial recognition technology, which is commonly used for identity verification and security purposes. In this paper, we propose a convolutional neural network (CNN) based approach to detect faces covered by surgical masks in medical settings. We evaluated the proposed CNN model on a test set comprising of masked and unmasked faces. The results showed that our model achieved an accuracy of over 96% in detecting masked faces. Furthermore, our model demonstrated robustness to different mask types and fit variations commonly encountered in medical settings. Our approaches reaches state of the art results in terms of accuracy and generalization. © 2022 Copyright for this paper by its authors.

Real-time Cheating Detection System Based on Sight Detection

Due to the COVID-19 pandemic, many exams, written tests and interviews are conducted online and remotely, which raises a series of questions such as how to prevent cheating. In this project, the methods commonly used in the existing cheating monitoring system are fully investigated and their shortcomings are improved one by one. Finally, a line of sight detection algorithm based on computer vision technology is designed, and a prototype of auxiliary cheating detection system that can get good results only with a small number of samples is developed. © 2022 SPIE.

COVID-19 X-RAY IMAGE DETECTION ALGORITHM BASED ON DEEP LEARNING

In order to enhance the ability to diagnose and distinguish COVID-19 from ordinary pneumonia, and to assist medical staff in chest X-ray detection of pneumonia patients, this paper proposes a COVID-19 X-ray image detection algorithm based on deep learning network. First of all, a model of deep learning network is set up based on VGG - 16, and then, the network structure and parameter optimization is adjusted, which makes the network model can be applied to COVID - 19 x ray imaging detection task. In the end, through adjusting the image size of the original data set, the input data meets the requirements of the deep learning network. Experimental results show that the proposed algorithm can effectively learn the characteristics of the COVID-19 X-ray image data set and accurately detect three states of COVID-19, common viral pneumonia and non-pneumonia, with a very high detection accuracy of 95.8%. © 2023 SPIE.

Person Detection Based Adaptive Video Synthesis: A Fusion Framework for Ultra HD Panorama and Green Screen Videos

The past two years have witnessed the increasing prevalence of metaverse, while the COVID-19 pandemic has accelerated the formation of a non-contact culture. Under this circumstance, virtual reality once again attracts the public attention. Panorama video, as one of the most important forms of virtual reality, provides users with excellent immersion experience. This paper has proposed a fusion framework for Ultra HD panorama videos and green screen videos. In this framework, panorama videos are set as the virtual background layer on which the user-defined real foreground layer of portrait obtained by green screen matting is superimposed. During video fusion process, the portrait size is adaptively determined by parameters provided by a person detection algorithm running on panorama videos. Therefore, a more natural video synthesis result can be achieved, and presented on a head-mounted display or a flat screen device to provide an indistinguishable visual experience to the users. © 2022 IEEE.

Mask Wear Detection Algorithm Based on YOLOv5m

Wearing a mask during the COVID-19 epidemic can effectively prevent the spread of the virus. In view of the problems of small target size, crowd blocking each other and dense arrangement of targets in crowded places, a target detection algorithm based on the improved YOLOv5m model is proposed to achieve efficient detection of whether a mask is worn or not. This paper introduces four attention mechanisms in the feature extraction network based on the YOLOv5m model to suppress irrelevant information, enhance the information representation of the feature map, and improve the detection capability of the model for small-scale targets. The experimental results showed that the introduction of the SE module increased the mAP value of the original network by 9.3 percentage points, the most significant increase among the four attention mechanisms. And then a dual-scale feature fusion network is used in the Neck layer, giving different weights to the feature layers to convey more effective feature information. In the image pre-processing, the Mosaic method was used for data enhancement, and the CIoU loss function was used for coordinate frame positioning in the prediction layer. Experiments on the improved YOLOv5m algorithm demonstrate that the mean recognition accuracy of the method improves by 10.7 percentage points over the original method while maintaining the original model size and detection speed, and better solves the problems of small scale, dense arrangement and mutual occlusion of targets in mask wearing detection tasks in crowded places. © 2023 IEEE.

The COVID'19 Dashboard with Implementation of Face Mask Detection and Social Distancing Detection Algorithm

With the outbreak of the global pandemic, India seemed to reach its peak with regard to the number of confirmed positive cases in the months of April and May. Hence, the decision was made to develop a data visualization project with one of the efficient visualization tools Tableau to help people analyze the scenario of the cases across the country. To contribute to state-wise and country-wise analysis of COVID cases in India, 2 dashboards have been developed. The first dashboard consists of the analysis of cases across the country giving a holistic and overall view of the number of deaths, positive cases, and density of cases in each state which is done through color variation. On the other hand, the second dashboard gives a detailed state-wise analysis of cases with the necessary parameters and details catering to every individual state as per the preference of the user. On merging these components, users can get an all-inclusive analysis based on different parameters on the COVID'19 cases across India at a glance. In order to prevent a further spike in cases, implementing a face mask detection system will also take place after conducting a thorough analysis of the possible machine learning algorithms. Two major object detection algorithms were taken into consideration and based on the conclusion drawn, the best algorithm - RCNN was used to implement the face mask detection system. This project is solely motivated by the current extreme situation in the world and as an attempt to provide a solution to combat the same. © 2023 IEEE.

Mask Recognition through Deep Learning

A major part of computer vision is formed by Object detection. Most of the such tasks are done with efficient object detection. This paper aims to incorporate techniques for facial mask detection to achieve an accurate and efficient mask detection algorithm. The goal is to examine various deep learning algorithms to perform mask detection in this era of Covid. This paper aims on building an application based on facial mask recognition using different deep learning algorithms and compare the results to find out the most accurate algorithm. © 2022 IEEE.

A more accurate mask detection algorithm based on Nao robot platform and YOLOv7

With the global outbreak of Corona Virus Disease 2019(COVID-19), many countries had made it mandatory for people to wear masks in public places. This paper proposed a novel mask detection algorithm RMPC (Restructing the Maxpool layer and the Convolution layer)-YOLOv7 based on YOLOv7 for detecting whether people wear masks in public places. The RMPC-YOLOv7 algorithm reconstructed the downsampling structure in the original YOLOv7 algorithm. We changed the stacking of the maxpooling layer and the convolutional layer. This enabled the feature information to be fully integrated to achieve the accuracy improvement of the new model. Through comparison experiments, our proposed RMPC-YOLOv7 had was improved 0.9% and 1.2% for mAP0.5 and mAP0.5:0.95, respectively. The experimental results demonstrated the feasibility of RMPC-YOLOv7. © 2023 IEEE.

Enhanced Automated Oxygen Level controller for COVID Patient By Using Internet of Things (IoT)

The Internet of Things (IoT) shall be merged firmly and interact with a higher number of altered embedded sensor networks. It provides open access for the subsets of information for humankind's future aspects and on-going pandemic situations. It has changed the way of living wirelessly, with high involvement and COVID-related issues that COVID patients are facing. There is much research going on in the recent domain, like the Internet of Things. Considering the financial-economic growth, there isn't much significance as IoT is growing with industry 5.0 as the latest version. The newly spreading COVID-19 (Coronavirus Disease, 2019) will emphasize the IoT based technologies in a greater impact. It is growing with an increase in productivity. In collaboration with Cloud computing, it shows wireless communication efficiently and makes the COVID-19 eradication in a greater way. The COVID-19 issues which are faced by the COVID patients. Many patients are suffering from inhalation because of lung problems. The second wave attacks mainly on the lungs, where there is a shortage of breathing problems because of less supply of oxygen (insufficient amount of oxygen). The challenges emphasized as proposed are like the shortage of monitoring the on-going process. Readily being active in this pandemic situation, the mentioned areas are from which need to be discussed. The frameworks and services are given the correct data and information for supply of oxygen to the COVID patients to an extent. The Internet of Things also analyzes the data from the user perspective, which will later be executed for making on-demand technology more reliable. The outcome for the COVID-19 has been taken completely to help the on-going COVID patients live, which can be monitored through Oxygen Concentration based on the IoT framework. Finally, this article discusses and mentions all the parameters for COVID patients with complete information based on IoT. © 2022 IEEE.

A Face Mask Detection Algorithm Based on YOLOv5

As a new machine learning method, deep learning has been widely used in computer vision. YOLOv5, a target detection algorithm based on deep learning, has a good detection effect. In the case of COVID-19, masks should be worn correctly in public places. Therefore, it is urgent to design an accurate and effective face mask detection algorithm. To solve the problem of mask-wearing detection, a face mask detection algorithm based on YOLOv5 is proposed. The main research contents include training of the YOLOv5 model, verification of face mask detection function, and analysis and comparison of detection effects of three different sizes of detection models: YOLOv5s, YOLOv5m and YOLOv5l. The proposed model realizes the mask detection function and obtains the advantages and disadvantages of different scale models through performance evaluation. The maximum mAP of the model reached 88.1%, with good detection accuracy. © 2022 IEEE.

A YOLOv5-based Target Detection Algorithm Design

At the beginning of 2020 Gengzi, a new coronavirus pneumonia (COVID - 19) that swept the world from the sky ravaged the land of God. In order to effectively organize the massive spread of the epidemic, this paper proposes a system that combines YOLOv5 to provide detection of faces wearing masks. The system is in a situation where one or more persons wearing masks in different scenarios can be detected. The design first uses a collection of mask face data under a variety of different wearing conditions and obtains a trained detection model using the above method to achieve the detection of whether a face is wearing a mask. The detection system can effectively detect the face mask wearing situation detected in the local picture elements, local video elements and the camera real- time shooting screen. The recognition effect of the system is verified to be 0.945, which is a significant improvement compared with other algorithms. © 2022 IEEE.

Detecting Drift in Healthcare AI Models Based on Data Availability

There is an increasing interest in the use of AI in healthcare due to its potential for diagnosis or disease prediction. However, healthcare data is not static and is likely to change over time leading a non-adaptive model to poor decision-making. The need of a drift detector in the overall learning framework is therefore essential to guarantee reliable products on the market. Most drift detection algorithms consider that ground truth labels are available immediately after prediction since these methods often work by monitoring the model performance. However, especially in real-world clinical contexts, this is not always the case as collecting labels is often more time consuming as requiring experts' input. This paper investigates methodologies to address drift detection depending on which information is available during the monitoring process. We explore the topic within a regulatory standpoint, showing challenges and approaches to monitoring algorithms in healthcare with subsequent batch updates of data. This paper explores three different aspects of drift detection: drift based on performance (when labels are available), drift based on model structure (indicating causes of drift) and drift based on change in underlying data characteristics (distribution and correlation) when labels are not available. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN

Since 2019, the coronavirus disease-19 (COVID-19) has been spreading rapidly worldwide, posing an unignorable threat to the global economy and human health. It is a disease caused by severe acute respiratory syndrome coronavirus 2, a single-stranded RNA virus of the genus Betacoronavirus. This virus is highly infectious and relies on its angiotensin-converting enzyme 2-receptor to enter cells. With the increase in the number of confirmed COVID-19 diagnoses, the difficulty of diagnosis due to the lack of global healthcare resources becomes increasingly apparent. Deep learning-based computer-aided diagnosis models with high generalisability can effectively alleviate this pressure. Hyperparameter tuning is essential in training such models and significantly impacts their final performance and training speed. However, traditional hyperparameter tuning methods are usually time-consuming and unstable. To solve this issue, we introduce Particle Swarm Optimisation to build a PSO-guided Self-Tuning Convolution Neural Network (PSTCNN), allowing the model to tune hyperparameters automatically. Therefore, the proposed approach can reduce human involvement. Also, the optimisation algorithm can select the combination of hyperparameters in a targeted manner, thus stably achieving a solution closer to the global optimum. Experimentally, the PSTCNN can obtain quite excellent results, with a sensitivity of 93.65% ± 1.86%, a specificity of 94.32% ± 2.07%, a precision of 94.30% ± 2.04%, an accuracy of 93.99% ± 1.78%, an F1-score of 93.97% ± 1.78%, Matthews Correlation Coefficient of 87.99% ± 3.56%, and Fowlkes-Mallows Index of 93.97% ± 1.78%. Our experiments demonstrate that compared to traditional methods, hyperparameter tuning of the model using an optimisation algorithm is faster and more effective. © 2023 Centro Regional de Invest. Cientif. y Tecn.. All rights reserved.

An Automated Facemask Detection For Covid-19 Using Viola-Jones Algorithm

Corona Virus Disease (Covid-19) is a label species of the Corona virus family. It can cause a variety of illnesses, from the ordinary cold to advanced respiratory syndromes like Middle-East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). This virus is highly contagious and spreads due to the droplets produced by coughing and sneezing. Though there are several ways to prevent the transmission of Covid-19, one of the most important and effective way is using a face mask or a face shield. In this paper, we constructed face mask detection framework using Viola-Jones algorithm in order to recognize whether an individual is wearing a mask or not. This algorithm includes the selection of Haar features of a face, integral image creation, adaptive boost training and cascading. An extensive study is carried out in order to analyze the performance of the proposed approach;we use a large facial image dataset from the publicly available MAFA dataset. The results indicate the proposed method can accurately identify face mask wearing images with a classifier accuracy of 98.26%, suggesting it might be useful in Covid-19 prevention. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Evaluation of YOLO-X and MobileNetV2 as Face Mask Detection Algorithms

Even though COVID-19 still exists, people are more reluctant to wear masks in public places, in fact only 73% of Indonesian still do. Hence, automatic mask surveillance in public places is still needed. In this paper, we compare two algorithms named YOLO-X and MobileNetV2 to detect face masks. YOLO-X was able to outperform other YOLO algorithms in object detection. While, according to researchers, MobileNetV2 achieved 9S% in face mask detection. To fairly evaluate both algorithms we need to conduct research under controlled variables including using the same datasets and devices. We used public datasets which consists of 1493 mask images and 6451 non mask images for training and testing. The results show that YOLO-X outperforms MobileNetV2 as it achieves 95.0%, 98.7%, 93.7%, and 96.1% for accuracy, average precision, recall, and F1-score respectively. YOLO-X also performs better in detecting faces with occlusion such as glasses, hands, and postures than MobileNetV2. However, YOLO-X detects faces and face masks 31.9% slower than MobileNetV2. © 2022 IEEE.

A Template Matching Based Cough Detection Algorithm Using IMU Data From Earbuds

Coughing is a common symptom across different clinical conditions and has gained further relevance in the past years due to the COVID-19 pandemic. An automated cough detection for continuous health monitoring could be developed using Earbud, a wearable sensor platform with audio and inertial measurement unit (IMU) sensors. Though several previous works have investigated audio-based automated cough detection, audio-based methods can be highly power-consuming for wearable sensor applications and raise privacy concerns. In this work, we develop IMU-based cough detection using a template matching-based algorithm. IMU provides a low-power privacy-preserving solution to complement audio-based algorithms. Similarly, template matching has low computational and memory needs, suitable for on-device implementations. The proposed method uses feature transformation of IMU signal and unsupervised representative template selection to improve upon our previous work. We obtained an AUC (AUC-ROC) of 0.85 and 0.83 for cough detection in a lab-based dataset with 45 participants and a controlled free-living dataset with 15 participants, respectively. These represent an AUC improvement of 0.08 and 0.10 compared to the previous work. Additionally, we conducted an uncontrolled free-living study with 7 participants where continuous measurements over a week were obtained from each participant. Our cough detection method achieved an AUC of 0.85 in the study, indicating that the proposed IMU-based cough detection translates well to the varied challenging scenarios present in free-living conditions. © 2022 IEEE.

A Face Mask Identification System based on the Internet of Things and Machine Learning for Detecting Covid-19

An appropriate mask protects individuals from infectious illness and greatly minimises the spread of COVID-2019 in public spaces like institutions and temples. This needs surveillance technology capable of detecting persons wearing correctly fitted masks. However, this is not the purpose of the face detection algorithms that are currently in use.The researchers suggest a two-stage technique for identifying mask wear using hybrid machine learning algorithms in this paper. The first step involves identifying as many possible candidate locations for wearing masks as possible employing Faster RCNN and ResNet V2 structures.In comparison, the second step entails employing a massive learning system to validate the real face masks. It is achieved by the training of a model with two classes. Additionally, this article describes a data collection conducted during the Market, Malls and contains 2804 realistic images. The suggested method exceeds all other techniques that are already in use, with an accuracy rate of 99.2 percent for straightforward circumstances. Copyright © 2022, Anka Publishers. All rights reserved.