Identification and Localization of COVID-19Abnormalities on Chest Radiographs using Computer Vision

The Corona Virus (COVID 19) pandemic is quickly becoming the world's most deadly disease. The spreading rate is higher and the early detection helps in faster recovery. The existence of COVID 19 in individuals shall be detected using molecular analysis or through radiographs of lungs. As time and test kit are limited RT- PCR is not suitable to test all. The RT- PCR being a time-consuming process, diagnosis using chest radiographs needs no transportation as the modern X-ray systems are digitized. Deep learning takes an edge over other techniques as it deduces the features automatically and performs massively parallel computations. Multiple feature maps will help in accurate prediction. The objective of the proposed work is to develop a Computer Aided Deep Learning System identify and localize COVID-19 virus from other viruses and pneumonia. It helps to detect COVID-19 within a short period of time thereby improving the lifetime of the individuals. SIIM-FISABIO-RSNA benchmark datasets are used to examine the proposed system. Recall, Precision, Accuracy-rate, and F-Measure are the metrics used to prove the integrity of the system. © 2023 IEEE.

Real Time Mask Detection using Convolutional Neural Network

With the advent of Convolutional Neural Networks, the field of image classification has seen tremendous growth, with various previously impossible applications now being pursued. One such application is face mask detection, which is an important problem to solve, considering recent pandemic. The novelty of this work is the training of YOLO (You Only Look Once) framework for custom object detection, which in this case is face mask, based on some empirical rules for fine-tuning the performance. Also, image classification is proposed to be combined with tracker, in order to implement real world access grant system based on compliance shown by mask wearer. © 2022 IEEE.

Face mask detection and counting using you only look once algorithm with Jetson Nano and NVDIA giga texel shader extreme

Deep learning and machine learning are becoming more extensively adopted artificial intelligence techniques for machine vision problems in everyday life, giving rise to new capabilities in every sector of technology. It has a wide range of applications, ranging from autonomous driving to medical and health monitoring. For image detection, the best reported approach is the you only look once (YOLO) algorithm, which is the faster and more accurate version of the convolutional neural network (CNN) algorithm. In the healthcare domain, YOLO can be applied for checking the face mask wearing of the people, especially in a public area or before entering any closed space such as a building to avoid the spread of the air-borne disease such as COVID-19. The main challenges are the image datasets, which are unstructured and may grow large, affecting the accuracy and speed of the detection. Secondly is the portability of the detection devices, which are generally dependent on the more portable like NVDIA Jetson Nano or from the existing computer/laptop. Using the low-power NVDIA Jetson Nano system as well as NVDIA giga texel shader extreme (GTX), this paper aims to design and implement real-time face mask wearing detection using the pretrained dataset as well as the real-time data. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

Image Recognition Based on Deep Learning with Thermal Camera Sensing

As the COVID-19 epidemic spread across the globe, people around the world were advised or mandated to wear masks in public places to prevent its spreading further. In some cases, not wearing a mask could result in a fine. To monitor mask wearing, and to prevent the spread of future epidemics, this study proposes an image recognition system consisting of a camera, an infrared thermal array sensor, and a convolutional neural network trained in mask recognition. The infrared sensor monitors body temperature and displays the results in real-time on a liquid crystal display screen. The proposed system reduces the inefficiency of traditional object detection by providing training data according to the specific needs of the user and by applying You Only Look Once Version 4 (YOLOv4) object detection technology, which experiments show has more efficient training parameters and a higher level of accuracy in object recognition. All datasets are uploaded to the cloud for storage using Google Colaboratory, saving human resources and achieving a high level of efficiency at a low cost. © 2023 CRL Publishing. All rights reserved.

Multiple face mask wearer detection based on YOLOv3 approach

The coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the SARS-CoV-2 coronavirus. In breaking the transmission chain of SARS-CoV-2, the government has made it compulsory for the people to wear a mask in public places to prevent COVID-19 transmission. Hence, an automated face mask detection is crucial to facilitate the monitoring process in ensuring people to wear a face mask in public. This project aims to develop an automated face and face mask detection for multiple people by applying deep learning-based object detection algorithm you only look once version 3 (YOLOv3). YOLOv3 object detection algorithm was concatenated with different backbones including ResNet-50 and Darknet-53 to develop the face and face mask detection model. Datasets were collected from online resources including Kaggle and Github and the images were filtered and labelled accordingly. The models were trained on 4393 images and evaluated based on precision, recall, mean average precision and the detection time. In conclusion, DarkNet53_YOLOv3 was chosen as the better model compared to ResNet50_YOLOv3 model with its good performance on accuracy with a mAP of 95.94% and a fast detection speed with a detection time of 50 seconds on 776 images.

A Deep Learning Approach for Detecting Face Mask Using an Improved Yolo-V2 With Squeezenet

Face mask detection has become a critical issue in security and Covid-19 prevention. In this regard, the YOLO V2 network has demonstrated outstanding performance. The YOLO V2 on the other hand, employed Darknet as a feature extractor. However, as compared to Darknet, SqueezeNet allows us to reduce model size while reaching or surpassing the highest accuracy score. SqueezeNet is designed to have lower parameters that can be more readily stored in computer memory and transferred across a computer network. As a result, in this study, we recommended enhancing the YOLO network by replacing Darknet with Squeezenet. Compared to other existing face mask recognition systems that use the standard YOLO V2 algorithm, this improves overall performance in terms of model size and accuracy. As a result, this study proposed a rapid face mask detection model by improving the existing YOLO V2 network architecture by employing logistic classifiers and SqueezeNet for multi-label classification using FMD and MMD face-masked dataset. The model was evaluated on MATLAB 2021 against state-of-the-art approaches. The proposed model outperforms previous algorithms by attaining a good accuracy value of 81% and a recall value of 99.99%. © 2022 IEEE.

Multiple face mask wearer detection based on YOLOv3 approach

The coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the SARS-CoV-2 coronavirus. In breaking the transmission chain of SARS-CoV-2, the government has made it compulsory for the people to wear a mask in public places to prevent COVID-19 transmission. Hence, an automated face mask detection is crucial to facilitate the monitoring process in ensuring people to wear a face mask in public. This project aims to develop an automated face and face mask detection for multiple people by applying deep learning-based object detection algorithm you only look once version 3 (YOLOv3). YOLOv3 object detection algorithm was concatenated with different backbones including ResNet-50 and Darknet-53 to develop the face and face mask detection model. Datasets were collected from online resources including Kaggle and Github and the images were filtered and labelled accordingly. The models were trained on 4393 images and evaluated based on precision, recall, mean average precision and the detection time. In conclusion, DarkNet53_YOLOv3 was chosen as the better model compared to ResNet50_YOLOv3 model with its good performance on accuracy with a mAP of 95.94% and a fast detection speed with a detection time of 50 seconds on 776 images.

Real-time multiple face mask and fever detection using YOLOv3 and TensorFlow lite platforms

COVID-19 has caused disruptions to many aspects of everyday life. To reduce the impact of this pandemic, its spreading must be controlled via face mask wearing. Manually mask-checking for everybody is embarrassing and uncontrollable. Hence, the proposed technique is used to help for automatic mask-checking based on deep learning platforms with real-time surveillance live infra-red (IR) camera. In this paper, two recent object detection platforms, named, you only look once version 3 (YOLOv3) and TensorFlow lite are adopted to accomplish this task. The two models are trained with a dataset consisting of images of persons with/without masks. This work is simulated with Google Colab then tested in real-time on an embedded device mated with fast GPU called Raspberry Pi 4 model B, 8 GB RAM. A comparison is made between the two models to verify their performance in relation to their precision rate and processing time. The work of this paper is also succeeded to realize multiple face masks real-time detection up to 10 facemasks in a single scene with high inference speed. Temperature is also measured using IR touchless sensor for each person with sound alarming to alert fever. The presented detector is cheap, light, small, and fast, with 99% accuracy rate during training and testing. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

IoT and AI-enabled Physical Distance Monitoring Application to Prevent COVID19 Transmission

During COVID19 pandemic, people are encouraged to practice physical distancing at least 1 meter when interacting with other people to prevent the spread of the COVID19. This study aims to develop a system that can monitor the physical distancing and track physical contact in a room using internet of things (IoT) and artificial intelligent technology. The system consists of a small single-board computer (Raspberry Pi), webcam, and web application displaying physical contact information. The system uses YOLO algorithms to detect the human object and euclidean distance formula to determine the distance between human objects. We evaluated the performance of YOLOv3 and YOLOv3-tiny running on Raspberry Pi. The evaluation result shows that YOLOv3 consumes more CPU resources than YOLOv3-tiny but has better accuracy in detecting human objects. YOLOv3-tiny can process images and detect objects faster than YOLOv3. © 2022 IEEE.

Face Mask and Social Distance Monitoring via Computer Vision and Deployable System Architecture

The coronavirus (COVID-19) is a lethal virus causing a rapidly infec-tious disease throughout the globe. Spreading awareness, taking preventive mea-sures, imposing strict restrictions on public gatherings, wearing facial masks, and maintaining safe social distancing have become crucial factors in keeping the virus at bay. Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus, the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly. To fight the spread of this virus, technologically developed systems have become very useful. However, the implementation of an automatic, robust, continuous, and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community. This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system. A modified version of a convolutional neural network, the ResNet50 model, has been utilized to identify masked faces in peo-ple. You Only Look Once (YOLOv3) approach is applied for object detection and the DeepSORT technique is used to measure the social distance. The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system, Jetson Nano edge computing device, and smartphones, Android and iOS applications. Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores. © 2023, Tech Science Press. All rights reserved.

A Multi-Mode Public Transportation System Using Vehicular to Network Architecture

The number of accidents in the campus of Suranaree University of Technology (SUT) has increased due to increasing number of personal vehicles. In this paper, we focus on the development of public transportation system using Intelligent Transportation System (ITS) along with the limitation of personal vehicles using sharing economy model. The SUT Smart Transit is utilized as a major public transportation system, while MoreSai@SUT (electric motorcycle services) is a minor public transportation system in this work. They are called Multi-Mode Transportation system as a combination. Moreover, a Vehicle to Network (V2N) is used for developing the Multi-Mode Transportation system in the campus. Due to equipping vehicles with On Board Unit (OBU) and 4G LTE modules, the real time speed and locations are transmitted to the cloud. The data is then applied in the proposed mathematical model for the estimation of Estimated Time of Arrival (ETA). In terms of vehicle classifications and counts, we deployed CCTV cameras, and the recorded videos are analyzed by using You Only Look Once (YOLO) algorithm. The simulation and measurement results of SUT Smart Transit and MoreSai@SUT before the covid-19 pandemic are discussed. Contrary to the existing researches, the proposed system is implemented in the real environment. The final results unveil the attractiveness and satisfaction of users. Also, due to the proposed system, the CO2 gas gets reduced when Multi-Mode Transportation is implemented practically in the campus.

Accurate Real-Time Face Mask Detection Framework Using YOLOv5

After the COVID-19 pandemic, wearing a mask has become a must because it decreases the probability of infection by 68%. That is why a fast and accurate automatic mask detection is crucial to public institutions. In this paper, we present an accurate framework for real-time mask detection using YOLOv5 object detection algorithm. Our framework consists of four stages: image preprocessing by normalization and adding noise, adding negative samples and data augmentation then the detection core based on a modified version of YOLOv5. The proposed framework achieves 95.9% precision and 84.8% mean average precision using the Face Mask Detection dataset with a 10 milliseconds inference time. © 2022 IEEE.

A Crowd Alert System for Pandemic Control using Yolov4

The Covid-19 pandemic in the late 2019 caused the world to shut down. Even though it is recommended to reduce overcrowding it still cannot be avoided. This can cause the pandemic to spread even more, especially since offices, schools and colleges are slowly reopening. With image detection making huge breakthroughs in the last decade, modern image detection technologies can now be combined with the current hardware to combat problems like overcrowding, which massively spreads the pandemic. In this paper, the YOLO v4 algorithm has been used, which greatly speeds up the process of detection and improves the overall accuracy of the system. © 2022 IEEE.

Detecting Face Mask for Prevent COVID-19 Using Deep Learning: A Novel Approach

The recent times we have seen that not taking proper measures like wearing mask or sanitization is the leading cause of the spread of “COVID-19”. Despite so many rules and regulations around the globe regarding proper sanitization and wearing of a mask, many people tend to ignore wearing of facemask when they are in public places. Although vaccines like covishield, covaxin, AstraZeneca, Pfizer-BioNTech, sputnik, etc. have been developed and the no of cases are decreasing in some countries to stop further transmit of virus it is still necessary to wear a mask in public places. Technology to monitor whether a person is wearing a mask in a public place or not is needed. One potential device like a CCTV camera can be used in this case. We came up with an algorithm “MaskYolo” which can be integrated with CCTV cameras that can detect if a person is wearing a mask or not using You Only Look Once (YOLO) algorithms. For our work, we used YOLOv4 and compared it to its sibling YOLOv4 Tiny. The best precision reached was 93.9 for YOLOv4 and 88.7 for YOLOv4 Tiny. Overall YOLOv4 stands out in all aspects for our model “MaskYolo”. Thus, we can use “MaskYolo” and build a device that detects if a person is wearing a mask or not. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Real-time Social Distance Detection using YOLO-v5 with Bird-eye View Perspective to Suppress the Spread of COVID-19

The COVID-19 virus outbreak has continued to spread since the end of 2019 worldwide. All people also implement health protocols not to contract this disease. One of the health protocols that must be implemented is to limit interactions between humans to a length of 1-2 meters or what is usually done with social distancing. Social distance detection system to ensure that people do not violate social distancing could be a solution to this problem. Using the YOLO-v5 method, which is the latest version of the YOLO (You Only Look Once) method with a detection speed of up to 140 Frames Per Second (FPS) and 90 percent smaller than the previous version, this system detects people who violate social distancing and then gives a voice warning to keep their distance to avoid spreading the COVID-19 virus. The human detection rate in the detection system reaches 93, 5%, and the accuracy for social distance detection reaches 95%. Based on the research that has been done, it can be said that this system can work well for detecting social distance, but the detection will start detecting the distance between the camera and the object exceeding 10 meters. © 2022 IEEE.

Camera-based Human Counting for COVID-19 Capacity Restriction

In this paper, a Human Counting system is implemented for COVID-19 capacity restrictions. It was implemented using the deep learning model You Only Look Once version 3(YOLOv3) to detect and count the people in a room. The system also can monitor the social distancing between the people in the room while labeling each person as 'safe' or 'unsafe' depending on whether they respect the social distancing protocols that the World Health Organization recommended or not. To make the project user friendly, a Graphical User Interface (GUI) was implemented to allow the user to choose the source of their images that will be used as input to be processed by the system. An experiment was carried out to evaluate the performance of the system under different conditions and in different scenarios where the evaluation was done according to some metrics such as accuracy, precision and recall. The output results from this experiment were demonstrated in details and compared to a similar algorithm as both algorithms focused on people detection using images from an inclined camera. The results show an accuracy of 96% for detection and the number of people counted. © 2022 IEEE.

Detecting COVID-19 Opacity in X-ray Images Using YOLO and RetinaNet Ensemble

Coronavirus disease(COVID-19) is caused by SARS-COV-2 virus and has been declared as a pandemic. After almost two years of this pandemic, over five million people have lost their lives worldwide due to complications like pneumonia and acute respiratory distress syndrome. Many countries have already witnessed the second wave of pandemic and a huge loss of lives. One way to curb the disease spread is by timely and accurate diagnosis. X-rays and CT-scans can help a radiologist to detect the disease, but detecting COVID-19 on chest radiographs can become challenging as it has similarities with other bacterial and viral pneumonias. Hence, there is a need to develop an algorithm for accurate and fast detection of COVID-19 in a patient. This work showcases the use of object detection deep learning models-You Only Look Once (YOLO) and RetinaNet for accurate localization of regions associated with COVID-19. Proposed method using ensemble of both the models achieves a mean average precision (mAP) score of 0.552, offering an improvement over their individual predictions. © 2022 IEEE.

A Computer Vision-based System for Surgical Waste Detection

The world population is going through a difficult time due to the pandemic of COVID-19 while other disasters prevail. However, a new environmental catastrophe is coming because surgical masks and gloves are putting down anywhere, leading to the massive spreading of COVID-19 and environmental disasters. A significant number of masks and gloves are not properly managed. They are scattered around us such as roads, rivers, beaches, oceans and other places. Since these types of waste are turned into microplastics and chemicals are deadly harmful to the environment, human health and other species, especially for the aquatic animals on this planet. During the outbreaks of corona pandemic, surgical waste in the open place or seawater can create a fatal contagious environment. Putting them in a particular area can protect us from spreading infectious diseases. This study proposed a system that can detect surgical masks, gloves and infectious/biohazard symbols to put down infectious waste in a specific place or a container. Among the various types of surgical waste, this study prefers mask and gloves since it is currently the most widely used element due to the COVID-19. A novel dataset is created named MSG (Mask, Bio-hazard Symbol and Gloves), containing 1153 images and their corresponding annotations. Different versions of the You Only Look Once (YOLO) are applied as the architecture of this study;however, the YOLOX model outperforms.

COVID-19: Automatic Social Distancing Rule Voilation Detection using PP-Yolo Tensorflow in OpenCV

In this critical situations where people are fighting with dangerous pandemic disease;it is required to maintain the situation by indulging with social distancing or it can also be pronounced as physical distancing. Social or physical distancing may reflects to reduce the virus from spreading. There are several places where it should be followed properly to stop spreading COVID-19 like railway stations, malls, marts, airports and many more. It is advised to maintain at least 6 feet of social distancing as per the WHO guidelines. Various researches have been done to automatically detect the physical distancing violations but an ideal system should be available to detect it effectively with high level of accuracy. Here the system is based on PP-Yolo (PaddlePaddle - You only look once) and Tensorflow library. Tensorflow is an object detection or pattern recognition tool through which pedestrian can be detected automatically and then PP-Yolo classifies the distance between the pedestrians or classifying whether persons are following the physical distancing rule or not. Violation detection is bit challenging for any system because a crowd may have uncertain structures that can hardly classified distance among them. This challenge can be accepted through various researchers but not met the desired precision. Proposed system is intended to detect the physical distancing rule violations effectively and acquiring high level of accuracy with minimal false alarm rate. © 2022 IEEE.

A Review on Social Distancing Auto Detection techniques in perspective of COVID' 19

In late December 2019, a new strain of coronavirus disease was first discovered in Wuhan, China. In 2020, the virus became a global epidemic after just a few months. On May 2020, the World Health Organization (WHO) declared the outbreak as pandemic. The statistics by World Health Organization on 24th February 2021 confirm more than 115 million infected people and 2.5 million deaths in 200 countries. The scientists have developed the vaccines only a few months ago, before that the best way to avoid the spread ofCOVID-19 was to maintain a few feet physical distance from one another. Social distancing was one amongst the recommended remedies by the World Health Organization (WHO) to minimize the spread of COVID-19 in public areas. Government bodies and authorities have made the 6 feet social distance in public and enclosed areas such as school, shopping malls and transport facilities as mandatory. Even after so much effort from the government and World Health Organization, people do not maintain social distancing which leads to constant rise in the number of infected people. The models and methods proposed in this paper focus of automatic monitoring of social distancing, the models will be able to detect the distance between two people and raise the alarm if social distancing is not maintained.