scholarly journals Face Mask Detection and Alert System

2021 ◽  
Author(s):  
Shreya Khare ◽  
Shreya Mukherjee ◽  
Kausar Nifa Shaikh ◽  
Urvashi Patkar
Keyword(s):  
Neural Network ◽  
Face Mask ◽  
Locally Linear Embedding ◽  
Alert System ◽  
Know How ◽  
The Neural Network ◽  
Image Dataset ◽  
Linear Embedding ◽  
Cctv Surveillance ◽  
Day By Day

In today’s era, as we all know how the year 2020 has brought an alarming pandemic with it and day by day, we are reaching a new peak of COVID cases. And due to which a main contribution asked from all the citizens is to follow all the safety norms to soothe the condition. One of the norms states to wear facemask all the time immediately after stepping out of their home. This paper proposes one of the methods to ensure that at least all people coming under any Closed-Circuit Television (CCTV) surveillance wears masks and that too properly. In this system we are using locally linear embedding (LLE) algorithm for face detection and convolutional neural network (CNNs) to reconfigure the image to fit into the network. And the neural network is trained with the help of image dataset. The method attains training accuracy and validation accuracy up to 99.87% and 93.41% respectively on two different datasets. If the system found out a person with no mask or not wearing it properly an alarm buzz outs to alter.

The Research on Recognition of the Irregular Objects

2012 ◽  
Vol 241-244 ◽  
pp. 1602-1607
Author(s):  
Guang Hai Han ◽  
Xin Jun Ma
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Convergence Speed ◽  
Know How ◽  
The Neural Network ◽  
Variable Step ◽  
Adaptive Momentum ◽  
The Way

It usually need different ways to process different objects in the manufacturing, Therefore, firstly we need to distinguish the categories of objects to be processed, then the machine will know how to deal with the objects. In order to automatically recognize the category of the irregular object, this paper extracted the improved Hu's moments of each object as the feature by the way of processing images of the working platform that the irregular objects are putting on. This paper adopts the variable step BP neural network with adaptive momentum factor as the classifier. The experiment shows that this method can effectively distinguish different irregular objects, and during the training of the neural network, it has faster convergence speed and better approximation compared with the traditional BP neural network

Artificial neural networks in the problem of determining the position and size of the solar disk in wide-angle photographs of the sky

2021 ◽  
Author(s):  
Mikhail Borisov ◽  
Mikhail Krinitskiy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Disk ◽  
Weather Conditions ◽  
Wide Angle ◽  
Know How ◽  
The Neural Network ◽  
Artificial Neural ◽  
The Moment

<p>Total cloud score is a characteristic of weather conditions. At the moment, there are algorithms that automatically calculate cloudiness based on a photograph of the sky These algorithms do not know how to find the solar disk, so their work is not absolutely accurate.</p><p>To create an algorithm that solves this data, the data used, obtained as a result of sea research voyages, is used, which is marked up for training the neural network.</p><p>As a result of the work, an algorithm was obtained based on neural networks, based on a photograph of the sky, in order to determine the size and position of the solar disk, other algorithms can be used to work with images of the visible hemisphere of the sky.</p>

scholarly journals Bus Attendance System using Optical Character Recognition

2020 ◽  
Vol 9 (4) ◽  
pp. 2133-2136
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
License Plate ◽  
License Plate Recognition ◽  
Surveillance Camera ◽  
Optical Character ◽  
The Neural Network ◽  
Character Extraction ◽  
Image Dataset

In today’s world managing the records of attendance of staffs, students, employee or bus is a tedious task. This project focuses on automating the bus attendance process through vehicle license plate recognition. As, the license plate is a feature that is peculiar to every vehicle, it would help in efficiently marking the bus attendance. The bus attendance system using RFID is a time consuming process. Hence we developed a project to efficiently mark attendance using number plate recognition and OCR. The system was trained using faster RCNN model with bus image dataset. The proposed system is the number plate is captured through surveillance camera and the captured image will be passed as an input to the neural network for training and the number plate will be detected. Character extraction is done using OCR and extracted character matched will be checked with the database and the attendance for particular bus will be marked.

Conceptual Design of Freeform Surfaces From Unstructured Point Sets Using Neural Network Regression

2011 ◽  
Author(s):  
Mehmet Ersin Yumer ◽  
Levent Burak Kara
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Locally Linear Embedding ◽  
Point Sets ◽  
3D Space ◽  
Closed Surfaces ◽  
Test Models ◽  
Point Set ◽  
Linear Embedding ◽  
Trained Network

This paper presents a new point set surfacing method that employs neural networks for regression. Our technique takes as input unstructured and possibly noisy point sets representing two-manifolds in R3. To facilitate parametrization, the set is first embedded in R2 using neighborhood preserving locally linear embedding. A neural network is then constructed and trained that learns a mapping between the embedded 2D parametric coordinates and the corresponding 3D space coordinates. The trained network is then used to generate a tessellation that spans the parametric space, thereby producing a surface in the original space. This approach enables the surfacing of noisy and non-uniformly distributed point sets, and can be applied to open or closed surfaces. We show the utility of the proposed method on a number of test models, as well as its application to freeform surface creation in virtual reality environments.

scholarly journals Modelo para detectar el uso correcto de mascarillas en tiempo real utilizando redes neuronales convolucionales

2021 ◽  
Vol 9 (17) ◽  
pp. 111-120
Author(s):  
Hugo Andrade Carrera ◽  
Soraya Sinche Maita ◽  
Pablo Hidalgo Lascano
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Face Mask ◽  
Training Process ◽  
Public Places ◽  
The Neural Network ◽  
Great Performance ◽  
The Family ◽  
The Face ◽  
Mandatory Use

Since Covid-19 appeared, the world has entered into a new stage, in which everybody is trying to mitigate the effects of the virus. The mandatory use of face masks in public places and when maintaining contact with people outside the family circle is one of mandatory measures that many countries have implemented, such as Ecuador, thus, the purpose of this article is to develop a convolutional neural network model using TensorFlow based on MobileNetV2, that allows to perform mask detection in real time video with the key feature of determining if the person is using the face mask properly or if it is not wearing a mask, in order to use the model with OpenCV and a pretrained neural network that detects faces. In addition, the performance metrics of the neural network are analyzed, including precision, accuracy, recall and the F1 score. All performance metrics consider the number of epochs for the training process, obtaining as a result a model that classifies between three groups: faces without face mask, faces wearing a face mask improperly and faces wearing a mask properly. with a great performance in all metrics; The results show values greater than 85% for precision, recall and F1 score, and accuracy values between 93% for 5 epochs and 95% for 25 epochs.

scholarly journals Physical Distancing Device with Edge Computing for COVID-19 (PADDIE-C19)

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 279
Author(s):  
Chun Hoe Loke ◽  
Mohammed Sani Adam ◽  
Rosdiadee Nordin ◽  
Nor Fadzilah Abdullah ◽  
Asma Abu-Samah
Keyword(s):  
Neural Network ◽  
Close Contact ◽  
Low Cost ◽  
Absolute Error ◽  
Face Mask ◽  
Physical Distance ◽  
Edge Computing ◽  
Computing Device ◽  
Person Detection ◽  
The Neural Network

The most effective methods of preventing COVID-19 infection include maintaining physical distancing and wearing a face mask while in close contact with people in public places. However, densely populated areas have a greater incidence of COVID-19 dissemination, which is caused by people who do not comply with standard operating procedures (SOPs). This paper presents a prototype called PADDIE-C19 (Physical Distancing Device with Edge Computing for COVID-19) to implement the physical distancing monitoring based on a low-cost edge computing device. The PADDIE-C19 provides real-time results and responses, as well as notifications and warnings to anyone who violates the 1-m physical distance rule. In addition, PADDIE-C19 includes temperature screening using an MLX90614 thermometer and ultrasonic sensors to restrict the number of people on specified premises. The Neural Network Processor (KPU) in Grove Artificial Intelligence Hardware Attached on Top (AI HAT), an edge computing unit, is used to accelerate the neural network model on person detection and achieve up to 18 frames per second (FPS). The results show that the accuracy of person detection with Grove AI HAT could achieve 74.65% and the average absolute error between measured and actual physical distance is 8.95 cm. Furthermore, the accuracy of the MLX90614 thermometer is guaranteed to have less than 0.5 °C value difference from the more common Fluke 59 thermometer. Experimental results also proved that when cloud computing is compared to edge computing, the Grove AI HAT achieves the average performance of 18 FPS for a person detector (kmodel) with an average 56 ms execution time in different networks, regardless of the network connection type or speed.

scholarly journals Implementasi jaringan syaraf tiruan untuk menilai kelayakan tugas akhir mahasiswa (studi kasus di amik bukittinggi)

2017 ◽  
Vol 8 (1) ◽  
pp. 10-24
Author(s):  
Novia Lestari ◽  
Lucky Lhaura Van FC
Keyword(s):  
Neural Network ◽  
College Students ◽  
Assessment Process ◽  
Final Conclusion ◽  
Backpropagation Algorithm ◽  
Know How ◽  
The Neural Network

Abstrak- Masing-masing mahasiswa telah diberikan buku panduan penulisan tugas akhir untuk penyusunan tugas akhirnya. Namun masih ditemui beberapa perbedaan pada tugas akhir mahasiswa yang telah menyelesaikan tugas akhir tersebut. Sehingga, penilaian kelayakan tugas akhir perlu dilakukan guna memperoleh hasil yang baik dan sesuai dengan format yang ada, serta layak dipublikasikan sesuai kriteria atau ketentuan yang telah ditetapkan. Untuk mempercepat proses penilaian dan pengambilan keputusan apakah tugas akhir yang dinilai tersebut layak atau tidak, tim penilai terkadang hanya melihat hasil secara menyeluruh sebagai acuan, sehingga hasil penilaianpun tidak bisa dipastikan dengan benar dan tidak objektif. Penelitian ini akan mengimplementasikan jaringan syaraf tiruan menggunakan algoritma BackPropagation untuk menilai kelayakan tugas akhir mahasiswa dengan menggunakan software Matlab 6.1. Pengujian akan dilakukan dengan berbagai pola untuk membandingkan hasil dari jaringan syaraf tiruan tersebut, agar mendapatkan hasil penilaian yang optimal apakah tugas akhir yang dinilai tersebut layak atau tidak. Kata Kunci : backpropagation, Jaringan Syaraf Tiruan, Keputusan, Tugas Akhir Abstract- Each student has been given a guide book as the guidelines to their final assignment. But in fact, the students still face the difficulties in following the guidelines and finishing their final assignment. It caused their final assignment need to be evaluate based on the format. In increasing the assessment process on how effective and proper of the assignment, usually could be found through the final conclusion of their final assignment. It may caused some mistaken assessment in objectivity. This research will be implement the neural network by using BackPropagation Algorithm in order to know how effective it is, based on the final assignment of the college students through Matlab 6.1 Software. The assessment will use some methods in comparing of the neural network, to find the final conclusion about the reasonable of the research. Keywords: Backpropagation, Decision, Final Assignment, Neural Network

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