Embedded system vehicle based on multi-sensor fusion

As intelligent driving vehicles came out of concept into people’s life, the combination of safe driving and artificial intelligence becomes the new direction of future transportation development. Autonomous driving technology is developing based on control algorithms and model recognitions. In this paper, a cloud-based interconnected multi-sensor fusion autonomous vehicle system is proposed that uses deep learning (YOLOv4) and improved ORB algorithms to identify pedestrians, vehicles, and various traffic signs. A cloud-based interactive system is built to enable vehicle owners to master the situation of their vehicles at any time. In order to meet multiple application of automatic driving vehicles, the environment perception technology of multi-sensor fusion processing has broadened the uses of automatic driving vehicles by being equipped with automatic speech recognition (ASR), vehicle following mode and road patrol mode. These functions enable automatic driving to be used in applications such as agricultural irrigation, road firefighting and contactless delivery under new coronavirus outbreaks. Finally, using the embedded system equipment, an intelligent car was built for experimental verification, and the overall recognition accuracy of the system was over 96%. Author

Social Distancing Monitoring and Contact Tracing on CCTV Footage

Social distancing is one of the most effective measures to prevent the spread of the COVID-19 disease. Most methods of enforcing this in the Philippines resort to manual methods. As such, a video-based social distancing monitoring tool can help ensure constant enforcement of social distancing due to the availability and up-time of CCTV cameras in various areas. This can be achieved by using object detection and tracking techniques. Object detection can be used to detect people within an area, and tracking can be used to watch people who get into close contact with one another. Contact tracing can also be performed by processing the social distancing measurements and tracking information. This information can be stored to keep a record of who has a high risk of infection based on who they came into contact with and for how long. We introduce a social distancing monitoring and contact tracing framework using the EfficientDet object detector and DeepSORT tracker. This framework is used to monitor social distancing violations and keep a record of violations associated to the tracked people. © 2022 IEEE.

Real-Time and Accurate Monitoring of Social Distance and Contact Tracing during COVID-19 Situation

In recent years, a lot of research works have been done on object detection using various machine learning models. However, not many works have been done on detecting and tracking humans in particular. This study works with the YOLOv4 object detector to detect humans to use the detections for maintaining social distance. For this study, the YOLOv4 model is trained on only one class named 'Person'. This is done to improve the speed of detecting humans in real time scenario with satisfying accuracy of 97% to 99%. These detections are then tracked to build a system for maintaining social distance and alerting the authority if a breach in the social distance is detected. This system can be applied at ticket counters, hospitals, offices, factories etc. It can also be used for maintaining social distance among the students and the teachers in the classroom for their safety. © 2022 IEEE.

YOLOv7 for Face Mask Identification Based on Deep Learning

The World Health Organization (WHO) has publicized a global public health emergency due to the COVID-19 coronavirus pandemic. Wearing a mask in public can provide protection against the spread of disease. Tremendous progress has been made in object detection in recent times, thanks in large part to deep learning models, which have shown encouraging results when it comes to recognizing objects in images. Recent technological developments have made this progress possible. Wearing a mask in public is one way to prevent the transmission of COVID-19 from others. Our study employs You Only Look Once (YOLO) v7 to determine whether a subject is wearing a mask, and then divides them into three groups depending on the degree to which they are wearing a mask correctly (none, bad, and good). In this study, we merged two datasets, the Face Mask Dataset (FMD) and the Medical Mask Dataset (MMD), to conduct our experiment. These models' evaluations and ratings include crucial criteria. According to our data, YOLOv7 achieves the highest mAP (98.5%) in the "Good"class. © 2023 IEEE.

OutCLIP, A New Multi-Outfit CLIP Based Triplet Network

Choosing a proper outfit is one of the problems we deal with every day. Today, people tend to use online websites for shopping, and the COVID-19 situation forced this condition more than before. In this research, we proposed a new architecture for multi-fashion item retrieval from a website database. We deployed a CLIP transformer model instead of convolutional neural networks in a triplet network. We also added a long short-term memory network (LSTM) to automatically extract and code the image features to generate descriptive text for each input image. Our OutCLIP model succeeded in doing its task with 83% precision and 85% recall accuracy in multi-item retrieval. This model can be trained and used in fashion retrieval problems and improve the former proposed models. Considering the descriptive text and the image together gives the model a better understanding of the concept and improves its generalization. © 2023 IEEE.

Joystick Controlled Autonomous Robotic Arm Using Inverse Kinematics with Object Detection

In 2019, the Novel Coronavirus Disease (COVID-19) was categorized as a pandemic. This disease can be transmitted via droplets on items or surfaces within several hours. Therefore, the researchers aimed to develop a wirelessly controlled robot arm and platform capable of picking up objects detected via object detection. Robot arm movements are done via the use of inverse kinematics. Meanwhile, a custom object detection model that can detect objects of interest will be trained and implemented in this project. To achieve this, the researchers utilize various open-source libraries, microcontrollers, and readily available materials to construct and program the entire system. At the end of this research, the prototype could reliably detect objects of interest, along with a grab-and-dispose success rate of 88%. Instruction data can be properly sent and received, and dual web cam image transfer reaches up to 1.72 frames per second. © 2023 IEEE.

Design of People Count System Based on Object Detection Algorithm

At present, the Covid-19 epidemic is still spreading globally. Although the domestic epidemic has been well controlled, the prevention and control of the epidemic must not be taken lightly. Being able to count the number of people in public places in real time has played a vital role in the prevention and control of the epidemic. Deep learning networks usually cannot be directly deployed on embedded devices with low computing power due to the huge amount of parameters of convolutional neural networks. This article is based on the YOLOv5 object detection algorithm and Jetson Nano embedded platform with TensorRT and C++ accelerating, it can realize the function of counting the number of people in the classroom, on the elevator entrance, and other scenes. © 2022 SPIE.

Technology-Driven Implementation of Smart Entrances in Public Places during the COVID-19

This research proposes a smart entrance system to cope with the COVID-19 pandemic in public places. The system can help automate standard operating procedures (SOPs) for checking. The paper focuses on exploring the problem context related to the COVID-19 SOPs for public places. The research on technologies involves using thermal cameras, fingerprint recognition, face recognition, iris recognition, object detection and cloud computing. These technologies can be integrated to provide a more versatile and effective solution. The technological solutions proposed by contemporary researchers are also critically analysed by investigating their advantages and disadvantages. © 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.

PACMAN: A Framework for Pulse Oximeter Digit Detection and Reading in A Low-Resource Setting

In light of the COVID-19 pandemic, patients were required to manually input their daily oxygen saturation (SpO2) and pulse rate (PR) values into a health monitoring system—unfortunately, such a process trend to be an error in typing. Several studies attempted to detect the physiological value from the captured image using optical character recognition (OCR). However, the technology has limited availability with high cost. Thus, this study aimed to propose a novel framework called PACMAN (Pandemic Accelerated Human-Machine Collaboration) with a low-resource deep learning-based computer vision. We compared state-of-the-art object detection algorithms (scaled YOLOv4, YOLOv5, and YOLOR), including the commercial OCR tools for digit recognition on the captured images from the pulse oximeter display. All images were derived from crowdsourced data collection with varying quality and alignment. YOLOv5 was the best-performing model against the given model comparison across all datasets, notably the correctly orientated image dataset. We further improved the model performance with the digits auto-orientation algorithm and applied a clustering algorithm to extract SpO2 and PR values. The accuracy performance of YOLOv5 with the implementations was approximately 81.0-89.5%, which was enhanced compared to without any additional implementation. Accordingly, this study highlighted the completion of the PACMAN framework to detect and read digits in real-world datasets. The proposed framework has been currently integrated into the patient monitoring system utilized by hospitals nationwide. IEEE

Social Distance Monitoring Framework Using YOLO V5 Deep Architecture

Due to the current outburst and speedy spread of the COVID-19 pandemic, there is a need to comply with social distancing rules by the general public. The public needs to, at minimum, hold a distance of 3 ft or 1 m among one another to follow strict social distancing as instructed by using the World Health Organization for general public safety. Researchers have proposed many solutions based on deep learning to reduce the current pandemic, including COVID-19 screening, diagnosis, social distancing monitoring, etc. This work focuses explicitly on social distancing monitoring by a deep learning approach. Here we employ the YOLOV5 object detection technique upon different images and videos to develop a strategy to assist and put strict social distancing in public. The YOLOV5 algorithm is more robust and has a quicker detection pace than its competitors. The suggested object detection framework shows an average precision rating of 94.75%. Some of the existing analyses suffer to identify humans within a range. A few identification blunders happen because of overlapping video frames or humans taking walks too near each other. This detection mistake is due to the overlapping structures, and human beings are standing too close to each other. This paper focuses on correctly identifying humans by using and overcoming the flaws of frame overlapping. Following the proposed social distancing technique also yields positive results in numerous variable eventualities. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 Face Mask Classification Using Deep Learning

The COVID-19 pandemic has triggered a global health disaster because its virus is spread mainly through minute respiratory droplets from coughing, sneezing, or prolonged close contact between individuals. Consequently, World Health Organization (WHO) urged wearing face masks in public places such as schools, train stations, hospitals, etc., as a precaution against COVID-19. However, it takes work to monitor people in these places manually. Therefore, an automated facial mask detection system is essential for such enforcement. Nevertheless, face detection systems confront issues, such as the use of accessories that obscure the face region, for example, face masks. Even existing detection systems that depend on facial features struggle to obtain good accuracy. Recent advancements in object detection, based on deep learning (DL) models, have shown good performance in identifying objects in images. This work proposed a DL-based approach to develop a face mask detector model to categorize masked and unmasked faces in images and real-time streaming video. The model is trained and evaluated on two different datasets, which are synthetic and real masked face datasets. Experiments on these two datasets showed that the performance accuracy rate of this model is 99% and 89%, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Face Mask Detection Using YOLOv3

The COVID-19 is an unprecedented crisis that has resulted in several security issues and large number of casualties. People frequently use masks to protect themselves against the transmission of coronavirus. In view of the fact that specific aspects of the face are obscured, facial identification becomes extremely difficult. During the ongoing coronavirus pandemic, researchers' primary focus has been to come up with suggestions for dealing with the problem through rapid and efficient solutions, as mask detection is required in the current scenario, whether in public or in some institutions such as offices and other workplaces. Only detecting whether a person wears mask or not is not enough. There is another aspect of wearing the mask properly such that it covers all the required portion of the face to ensure there is no exposure to any viruses. To address this, we proposed a reliable technique based on image classification and object localization, which can be accomplished using YOLO v3's object detection in machine learning. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Object detection and activity recognition in video surveillance using neural networks

Purpose: This paper aims to implement and extend the You Only Live Once (YOLO) algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. Video Surveillance has many applications such as Car Tracking and tracking of people related to crime prevention. This paper provides exhaustive comparison between the existing methods and proposed method. Proposed method is found to have highest object detection accuracy. Design/methodology/approach: The goal of this research is to develop a deep learning framework to automate the task of analyzing video footage through object detection in images. This framework processes video feed or image frames from CCTV, webcam or a DroidCam, which allows the camera in a mobile phone to be used as a webcam for a laptop. The object detection algorithm, with its model trained on a large data set of images, is able to load in each image given as an input, process the image and determine the categories of the matching objects that it finds. As a proof of concept, this research demonstrates the algorithm on images of several different objects. This research implements and extends the YOLO algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. For video surveillance of traffic cameras, this has many applications, such as car tracking and person tracking for crime prevention. In this research, the implemented algorithm with the proposed methodology is compared against several different prior existing methods in literature. The proposed method was found to have the highest object detection accuracy for object detection and activity recognition, better than other existing methods. Findings: The results indicate that the proposed deep learning–based model can be implemented in real-time for object detection and activity recognition. The added features of car crash detection, fall detection and social distancing detection can be used to implement a real-time video surveillance system that can help save lives and protect people. Such a real-time video surveillance system could be installed at street and traffic cameras and in CCTV systems. When this system would detect a car crash or a fatal human or pedestrian fall with injury, it can be programmed to send automatic messages to the nearest local police, emergency and fire stations. When this system would detect a social distancing violation, it can be programmed to inform the local authorities or sound an alarm with a warning message to alert the public to maintain their distance and avoid spreading their aerosol particles that may cause the spread of viruses, including the COVID-19 virus. Originality/value: This paper proposes an improved and augmented version of the YOLOv3 model that has been extended to perform activity recognition, such as car crash detection, human fall detection and social distancing detection. The proposed model is based on a deep learning convolutional neural network model used to detect objects in images. The model is trained using the widely used and publicly available Common Objects in Context data set. The proposed model, being an extension of YOLO, can be implemented for real-time object and activity recognition. The proposed model had higher accuracies for both large-scale and all-scale object detection. This proposed model also exceeded all the other previous methods that were compared in extending and augmenting the object detection to activity recognition. The proposed model resulted in the highest accuracy for car crash detection, fall detection and social distancing detection. © 2023, Emerald Publishing Limited.

A Comprehensive Alert System Based on Social Distancing for Cautioning People Amidst the COVID-19 Pandemic Using Deep Neural Network Models

The World Health Organization (WHO) has suggested a successful social distancing strategy for reducing the COVID-19 virus spread in public places. All governments and national health bodies have mandated a 2-m physical distance between malls, schools, and congested areas. The existing algorithms proposed and developed for object detection are Simple Online and Real-time Tracking (SORT) and Convolutional Neural Networks (CNN). The YOLOv3 algorithm is used because YOLOv3 is an efficient and powerful real-time object detection algorithm in comparison with several other object detection algorithms. Video surveillance cameras are being used to implement this system. A model will be trained against the most comprehensive datasets, such as the COCO datasets, for this purpose. As a result, high-risk zones, or areas where virus spread is most likely, are identified. This may support authorities in enhancing the setup of a public space according to the precautionary measures to reduce hazardous zones. The developed framework is a comprehensive and precise solution for object detection that can be used in a variety of fields such as autonomous vehicles and human action recognition. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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.

Development of a Video Surveillance web Application for the use of Biosafety Equipment with Image Recognition for Workspaces

With the arrival of Covid-19, several preventive measures were implemented to limit the spread of this virus. Among these measures is the use of masks, both in open and closed public spaces. This measure has forced commercial establishments, workplaces, schools, hospitals, to maintain constant vigilance, upon entering their facilities, of the proper use of the mask, which should completely cover the nose, mouth and chin. However, this manual control is tedious and ineffective since most of the population is not able to correctly identify when a person has the mask on properly, with high error rates in the manual detection of the correct use of the mask according to surveys carried out. For this reason, this work proposes the automation of the detection of the proper use of the mask at the entrance to the work areas, also providing a follow-up panel of the recorded incidents. The effectiveness of the proposal was evaluated through the detection and categorization of a data set of more than 3000 images, resulting in an accuracy of 98.6%. © 2022 IEEE.

Computer-Aided System for COVID-19 Using Semi-supervised-based Ensemble Learning and Reinforcement Learning

Coronavirus Disease 2019(COVID-19) has shocked the world with its rapid spread and enormous threat to life and has continued up to the present. In this paper, a computer-aided system is proposed to detect infections and predict the disease progression of COVID-19. A high-quality CT scan database labeled with time-stamps and clinicopathologic variables is constructed to provide data support. To our knowledge, it is the only database with time relevance in the community. An object detection model is then trained to annotate infected regions. Using those regions, we detect the infections using a model with semi-supervised-based ensemble learning and predict the disease progression depending on reinforcement learning. We achieve an mAP of 0.92 for object detection. The accuracy for detecting infections is 98.46%, with a sensitivity of 97.68%, a specificity of 99.24%, and an AUC of 0.987. Significantly, the accuracy of predicting disease progression is 90.32% according to the timeline. It is a state-of-the-art result and can be used for clinical usage. © 2022 IEEE.

Real-Time Face Mask Detection using Computer Vision and Machine Learning

Face mask detection is the process of identifying whether a person is wearing a face mask or not in real-time through the use of computer vision and machine learning algorithms. This technology can be used in various applications, such as security systems at public transportation hubs or in hospitals, to ensure compliance with health and safety regulations during a pandemic or other infectious disease outbreaks. The technology works by analyzing images or video streams from cameras and computer vision techniques are used to detect the presence of a face mask on a person's face. The output of the system is a binary result (i.e., mask detected or not detected) or a more detailed result that provides information about the type of mask and its location on the face. © 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.