Health and energy are two important elements of life that must be constantly monitored. In the present pre-endemic or post-pandemic age, it is critical to monitor individuals to ensure that crucial standard operating procedures, such as wearing face masks, are followed. Furthermore, any measures to reduce energy use, particularly in public buildings that are now being utilised again in the postpandemic context, are critical. As a result, this study presents a live dashboard method for tracking both energy and face mask wear. The detection system is built on a Jetson Nano device and is then interfaced to a live dashboard to monitor people who wear masks, including those who wear them incorrectly. The observed findings for the confusion matrix and F1 score are also shown. At the same time, the suggested visual detection system is also capable of monitoring the energy use in the building where the device is mounted.
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 pre-trained dataset as well as the real-time data.
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