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Corona Virus is a pandemic, and the whole world is affected due to it. Apart from the vaccine, the only cure for this drastic disease is following the rules and regulations that prevent further spread. There are different mechanisms like (Social Distancing, Mask Detection, Human occupancy etc.) through which we can able to stop the spread of corona...
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Pavement cracks are severely affecting highway performance. Thus, implementing high-precision highway pavement crack detection is important for highway maintenance. However, the asphalt highway pavement environment is complex, and different pavement backgrounds are more difficult than others for detecting highway pavement cracks. Interference from...
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... To increase the accuracy factor, the authors must work on training and verification procedures for the dataset. In parallel, the most effective detection could be grasped with the objective research drives [34]. A dense connection strengthens the extracting feature and alleviates the vanishing gradient problem and also pools and concatenates the features of the multi-scale local region, to enhance comprehensive learning. ...
Computer vision and deep learning are emerging technologies as the backbone system to maintain the public healthcare sector to detect the object and surrounding, especially during the COVID-19 pandemic. Generally, in a single stage, you only look once version 3 (YOLOv3) algorithms promising the best results to detect the object in images, live feeds, or videos by learning features at a faster rate than two-stage algorithms such as R-CNN, fast CNN, and faster CNN. Deep sort methods were employed to track identified people by supporting bounding boxes and calculating the Euclidian distances between the people to maintain social distance. Moreover, the YOLOV3 model requires more computational cost to detect the object at best with a lower detection time. Hence, it motivates us to practice a single graphics processing unit (GPU) with the multithreaded approach to increase the frames per second at detection. The proposed model uses a background modeling method grounded on frame variance accumulation which is used to define the number of frames and weight updating. This approach uses two steps, localization of the object and then the classification of localized objects. Distances between people are calculated and compared with threshold values to facilitate comparison. The threshold limit triggers the alert system which is accessible to people, monitoring many video streams at a time. The model is tested based on processors, threads consumed, and various types of inputs ranging from static images to moving videos. Tiny-YOLOv3 performs with the best frames per second and the least processing time, followed by SPP-YOLOv3 and YOLOv3. The model proves its evidence on various parameters and metrics to work robustly. As well as the reason to adopt YOLOv3 over other YOLOv4 and YOLOV5 is tabulated. This model initiates the curiosity to develop a mobile application with security systems based on IoT and CCTV to monitor crowded places.
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... But this is not a totally helpful and practical solution as the whole economy of the particular country goes down. Especially it creates a catastrophic situation for underdeveloped countries in terms of economy (Khan et al., 2021). As of this writing, this virus has infected more than 380 million people, causing more than 5 million deaths worldwide. ...
This paper presents a deep learning approach for swift detection of COVID-19 in chest CT scan images in order to facilitate treatment planning and reduce the burden on hospital resources and staff workload. The detection procedure starts with a pre-processing step, which involves noise removal and resizing, and the pre-processed images are fed to VGG16, which is a powerful deep learning network for image classification applications. All algorithms have been implemented in Python and the deep learning network has been implemented in Tesorflow using the Keras library. Using VGG16, we have achieved 99% and 92% accuracy for the training and test data, respectively. Considering the accuracy of the method, it can be used for swift clinical detection of COVID-19, which could be of useful and magnificent help to treatment personnel. Also, this method is really helpful for detecting patient and starting treatment as soon as possible and reduces the cost of treatments.
... Punn et al. [8] used Yolov3 with deepsort tracking technique to detect people in order to monitor social distancing. Khan et al. [9] used Yolo, Faster-R-CNN, and SSD for identifying hotspots of people to mitigate the transmission of the coronavirus. ...
This paper outlines a technical method for video analysis that may be used to identify persons in footage from several CCTV cameras and provide a heatmap of that information for a certain floor layout. The analysis of customer and employee behavior in retail and office settings, as well as motion tracking and advertising effectiveness research, can all be aided by the automatic creation of people density maps. With the use of video recordings made by common video surveillance cameras, density maps were created. We made advantage of CCTV cameras, which are dispersed across a retail establishment. Because the Yolov5 object detection algorithm may produce findings more quickly, we have chosen to employ it for human detection. Additionally, due to the short inference time, it is appropriate for real-time applications.
... On the other hand, in order to verify that the Yolo algorithm worked correctly, an experiment was carried out consisting of recording a one-minute video, where the car navigates at three different speeds (1, 3 and 6.8 m/s). These videos were analyzed for each second giving the possibility of identifying false positives or false negatives, in order to make a confusion matrix (Khan et al. (2021); Sommer et al. (2020)) that will allow visualizing the performance of the algorithm in terms of object detection. ...
Within the Digital Twins context, efforts are being made to minimize costs, increase safety, and speed up tests within a specific application, based on computer graphics tools for three- dimensional simulations. Thus, as a way to mitigate mainly the risks with the safety issue involving activities with autonomous vehicles, the present work proposes the modeling and structuring of an electric golf cart in a 3D virtual environment, so that it can serve for studies in the areas of perception, navigation, and control. Therefore, taking as a reference the proper vehicle existing at the university, Blender software was used together with the Gazebo simulator to perform the validation of this simulation environment. Different open source Environment Mapping (SLAM), Pattern Recognition (YOLO), and Autonomous Navigation algorithms were integrated to minimize possible errors regarding their integration in the actual vehicle. Finally, validation tests were performed on the Yolo algorithm, resulting in an accuracy of 98.9% with a margin of error of 1.1% in the identification of objects.
... They used three pre-train CNNs, AlexNet, GoogleNet, and SqueezeNet, without data augmentation. They used six datasets and very high results were found [39]. CAD diagnosis methods are efficient methods to diagnose diseases with a comparison of other alternatives. ...
The early diagnosis and treatment of COVID-19 have been a challenge all over the world. It is challenging to manufacture many testing kits and even then, their accuracy rate is very low. Studies carried out recently show that chest x-ray images are of great help in the diagnosis of COVID-19. In this study, we have developed a COVID-19 detection model that by observing the chest x-ray images of the patient, detects whether either the patient is affected by COVID-19 or not. The model is developed using a custom Convolutional Neural Network (CNN) that differentiates between COVID-19 and healthy x-ray images so that the patient can be diagnosed and quarantined on time to prevent the spread of the pandemic. We used two different datasets which are publicly available for the training and validation of this model. Upon completion, the proposed model yields an accuracy of almost 98%. Upon further training, our model will be able to be used as a COVID-19 detection tool in hospitals worldwide and will play a vital role in early detection and timely containment of the pandemic.
... Deep learning has had a lot of success with human gait recognition in recent years. The convolutional neural network (CNN) is a type of deep learning model that is used for several processes, such as gait recognition [23], action recognition [24], medical imaging [25], and others [26,27]. A simple CNN model consists of a few important layers, such as convolutional, pooling, batch normalization, ReLu, GAP, fully connected, and classification layers [28,29]. ...
Gait is commonly defined as the movement pattern of the limbs over a hard substrate, and it serves as a source of identification information for various computer-vision and image-understanding techniques. A variety of parameters, such as human clothing, angle shift, walking style, occlusion, and so on, have a significant impact on gait-recognition systems, making the scene quite complex to handle. In this article, we propose a system that effectively handles problems associated with viewing angle shifts and walking styles in a real-time environment. The following steps are included in the proposed novel framework: (a) real-time video capture, (b) feature extraction using transfer learning on the ResNet101 deep model, and (c) feature selection using the proposed kurtosis-controlled entropy (KcE) approach, followed by a correlation-based feature fusion step. The most discriminant features are then classified using the most advanced machine learning classifiers. The simulation process is fed by the CASIA B dataset as well as a real-time captured dataset. On selected datasets, the accuracy is 95.26% and 96.60%, respectively. When compared to several known techniques, the results show that our proposed framework outperforms them all.
Due to the COVID-19 pandemic, there has been a huge impact worldwide. The transmission of COVID-19 can be prevented using preventive measures like social distancing and face masks. These measures could slow the spreading and prevent newer ones from occurring. Social distancing can be followed even by those with weaker immune systems or certain medical conditions. With the new normal into play, maintaining distance in social and wearing masks are likely to be followed for the next two years. This paper studies about maintaining distance in social and detection of masks using deep learning techniques. Several object detection models are used for detecting social distance. The inputs used are in the form of images and videos. With this system, the violations can be detected which will reduce the number of cases. In conclusion, the proposed system will be very efficient and can also be used to introduce newer preventive measures.
The unprecedented circumstances encountered during the COVID-19 pandemic in a variety of life aspects such as health, economy, and environment have motivated the humanity to devise new solutions to control and mitigate the impact of the pandemic. Originating from the high-accuracy decision-making capability of artificial intelligence, this paper aims to highlight the effectiveness of artificial intelligence applications in the areas of virus detection, health monitoring, face mask detection, crowd sensing, and satellite-based environment monitoring. One of the most promising deep learning techniques presented in the literature is the Convolutional Neural Network (CNN) that has shown remarkable classification accuracy. The reason for using artificial intelligence in this area is the inherent difficulty of these applications such as detecting COVID-19 infections using lung images due to its resemblance with other respiratory diseases. While the lack of sufficient training data is considered one of the main difficulties, it has been alleviated with the aid of specialized artificial intelligence techniques such as generative adversarial networks and transfer learning. Satellite-based imagery along with deep learning have shown an improvement in the air quality due to the imposed mobility restrictions during the pandemic. Apart from the technical challenges, some applications faced social and ethical challenges that are mainly related to the patient privacy. The latter factor have made dataset availability more limited, and restricted the implementation of some applications such as contact tracing. This work examines state-of-the-art studies and shows the effectiveness of artificial intelligence in solving the most challenging technical problems encountered during the pandemic. Impact Statement: Different technologies have been incorporated to slow down and control the COVID-19 pandemic, many of which involved artificial intelligence. The importance of artificial intelligence arises from the need to intelligently process data and help the authorities to make timely decisions. This paper presents a comprehensive review on the various applications of artificial intelligence during the COVID-19 pandemic with a focus on virus detection, face mask detection, crowd sensing, and satellite-based environment monitoring. The work highlights the problems, challenges, and implementation methodologies of artificial intelligence in a variety of applications that have helped the humanity during the pandemic.
