Internshipreport 2
Internshipreport 2
Internshipreport 2
CHAPTER 1:
COMPANY PROFILE
Vision is to provide training and implement services with edge of competitive in quality
& price to our valued customers in the ground of sustainability. We promote reputed multi
brand residential, commercial and Industrial automation products. Our focus and growth will
be on the technological outsourcing in the field of PLC controls, BMS, HVAC controls and
other related sub systems in the Middle East and Indian subcontinent.
1.1 MISSION
1.2 OBJECTIVES
Increase the technical knowledge and real time industrial exposure of the students
during the commencement of Industrial Training
Maximize student’s employability and increase the rate of placement
Branding of College &wider recognition by Industry and Corporate
Higher Ranking for the College in Surveys
Possibility of establishing the Centre of Excellence by the Industry
Certified and Industry oriented Technical Skills & Personality Development Training
under one window saving cost and Time
Live Projects and Practice on Real Systems and Instrument
CHAPTER 2:
The world has not yet fully Recover from this pandemic and the vaccine that can
effectively treat Covid-19 is yet to be discovered. However, to reduce the impact of the
pandemic on the country's economy, several governments have allowed a limited number of
economic activities to be resumed once the number of new cases of Covid19 has dropped below
a certain level. As these countries cautiously restarting their economic activities, concerns have
emerged regarding workplace safety in the new post-Covid-19 environment. To reduce the
possibility of infection, it is advised that people should wear masks and maintain a distance of
at least 1 meter from each other. Deep learning has gained more attention in object detection
and was used for human detection purposes and develop a face mask detection tool that can
detect whether the individual is wearing mask or not. This can be done by evaluation of the
classification results by analyzing real-time streaming from the Camera. In deep learning
projects, we need a training data set. It is the actual dataset used to train the model for
performing various actions.
The main objective of the face detection model is to detect the face of individuals and
conclude whether they are wearing masks or not at that particular moment when they are
captured in the image.
2.3 OBJECTIVES:
In the face detection method, a face is detected from webcam that has several attributes
in it.
Research into face detection requires expression recognition, face tracking and pose
estimation.
Here the main challenge of the task is to detect the face from the webcam and then
identify if it has a mask on it or not.
To Implement this case study, we need a lot of images of people wearing a mask and
not wearing a mask.
2.4 ADVANTAGES
Helps to prevent the spread of COVID-19: The primary benefit of face mask detection
is that it helps to prevent the spread of COVID-19. The technology can detect whether
a person is wearing a mask or not and alert them to wear one if they are not. This can
help reduce the risk of infection for individuals and communities.
Automates monitoring and enforcement: Face mask detection technology can automate
the monitoring and enforcement of mask-wearing policies in public places such as
airports, schools, and businesses. This can save time and resources, and ensure that
policies are being followed consistently.
Increases safety for employees and customers: Face mask detection technology can
create a safer environment for employees and customers by enforcing mask-wearing
policies. This can help reduce the risk of infection and increase confidence in the safety
measures being taken.
Improves compliance: Face mask detection technology can improve compliance with
mask-wearing policies by providing real-time feedback to individuals who are not
wearing a mask. This can encourage them to comply with the policy and reduce the
need for enforcement.
Supports public health initiatives: Face mask detection technology can support public
health initiatives by promoting the use of masks as an effective tool to prevent the
spread of infectious diseases. This can help raise awareness and encourage more people
to wear masks in public places.
2.5 APPLICATIONS
Face mask detection technology can be applied in various settings and industries to
enforce mask-wearing policies and promote public health. Here are some common
applications of face mask detection:
Face mask detection technology can be used in buses, trains, and subways to ensure
that all passengers are wearing masks. This can help reduce the risk of COVID-19
transmission in these high-density environments
Retail stores can use face mask detection technology to monitor compliance with mask-
wearing policies and enforce them when necessary. This can create a safer environment
for customers and employees.
Airports can use face mask detection technology to ensure that all passengers and
employees are wearing masks. This can help reduce the risk of COVID-19 transmission
during air travel.
Healthcare facilities can use face mask detection technology to monitor compliance
with mask-wearing policies among staff and visitors. This can help prevent the spread
of infectious diseases in these high-risk environments.
Schools and universities can use face mask detection technology to enforce mask-
wearing policies and ensure that all students, faculty, and staff are following them. This
can help prevent the spread of COVID-19 and other infectious diseases on campus.
Offices and workplaces can use face mask detection technology to enforce mask-
wearing policies among employees and visitors. This can create a safer environment
for everyone and reduce the risk of COVID-19 transmission in the workplace.
Sports and entertainment venues can use face mask detection technology to ensure that
all spectators and staff are wearing masks. This can help reduce the risk of COVID-19
transmission during large events.
CHAPTER 3:
LITERATURE SURVEY
CHAPTER 4:
4.2 RESULT:
CHAPTER 5:
BLOCK DIAGRAM
A face mask detection system as shown in the above figure typically involves the use
of a camera, an image processing algorithm, and a notification system to detect whether
individuals are wearing masks properly or not. Here is a block diagram that explains the
different components of a face mask detection system. The camera is the primary input device
that captures the image or video of individuals in a particular area. It is typically placed at an
appropriate location to capture the face of each person. The pre-processing module receives
the image or video feed from the camera and applies image processing techniques to improve
the quality of the images. This step is necessary to reduce noise, adjust brightness and contrast,
and improve image sharpness. The face detection module detects the faces of individuals in the
captured images. It uses algorithms such as Haar cascades,or deep learning-based models to
identify the regions of the image that correspond to the human face. The person face is detected
and labelled with the blue rectangular frame.
CHAPTER 6:
PROPOSED SYSTEM
This system is capable to train the dataset of both persons wearing masks and
without wearing masks.
After training the model the system can predicting whether the person is wearing themask
or not.
It also can access the webcam and predict the result.
6.2 OPENCV:
It is a cross-platform library using which we can develop real-time computer vision
applications.
It mainly focuses on image processing, video capture and analysis including feature like
face detection and object detection.
Currently Open CV supports a wide variety of programming languages like C++, Python,
Java etc. and is available on different platforms including Windows, Linux, OS X, Android,
iOS etc.
Also, interfaces based on CUDA and OpenCL are also under active development for
high-speed GPU operations. Open CV-Python is the Python API of Open CV.
It combines the best qualities of Open CV C++ API and Python language.
OpenCV (Open-Source Computer Vision Library) is an opensource computer vision and
machine learning software library. OpenCV was built to provide a common infrastructure
for computer vision applications
and to accelerate the use of machine perception in the commercial products. Being a BSD-
licensed product, OpenCV makes it easy for businesses to utilize and modify the code.
The library has more than 2500optimized algorithms, which includes a comprehensive set
of both classic and state-of -the-art computer vision and machine learning algorithms.
Algorithms can be used to detect and recognize faces, identify objects, classify human
actions in videos, track camera movements, track moving objects, extract 3D models of
objects, produce 3D point clouds from stereo cameras, stitch images together to produce a
high resolution image of an entire scene, find similar images from an image database,
remove red eyes from images taken using flash, follow eye movements, recognize scenery
and establish markers to overlay it with augmented reality, etc.
6.3 NUMPY:
NumPy is a library for the Python programming language, adding support for large,
multi-dimensional arrays and matrices, along with a large collection of high- level
mathematical functions to operate on these arrays. The ancestor of NumPy, Numeric, was
originally created by Jim Hugunin with contributions from several otherdevelopers. In 2005,
Travis Oliphant created NumPy by incorporating features of the competing Num array into
Numeric, with extensive modifications. NumPy is open- source software and has many
contributors.
The Python programming language was not initially designed for numericalcomputing,
but attracted the attention of the scientific and engineering community early on, so that a special
interest group called matrix-sig was founded in 1995 with the aim of defining an array
computing package. Among its members was Python designer and maintainer Guido van
Rossum, who implemented extensions to Python's syntax (in particular the indexing syntax) to
make array computing easier.
In early 2005, NumPy developer Travis Oliphant wanted to unify the community around
a single array package and ported num-array's features to Numeric, releasing the result as
NumPy 1.0 in 2006. This new project was part of SciPy. To avoidinstalling the large SciPy
package just to get an array object, this new package was separated and called NumPy.
6.4 KERAS
KERAS is an API designed for human beings, not machines. Keras follows best
practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the
number of user actions required for common use cases, and it provides clear & actionable error
messages.
It also has extensive documentation and developer guides. Keras contains numerous
implementations of commonly used neural network building blocks such as layers, objectives,
activation functions, optimizers, and a host of tools to make working with image and text data
easier to simplify the coding necessary for writing deep neural network code.
The code is hosted on GitHub, and community support forums include the GitHub issues
page, and a Slack channel. Keras is a minimalist Python library for deeplearning that can run
on top of Theano or Tensor Flow.
It was developed to make implementing deep learning models as fast and easy as
possible for research and development.
Machine learning (ML)is the study of computer algorithms that improve automatically
through experience. Itis seen as a subset of artificial intelligence. Machine learning algorithms
build a mathematical model based on sample data, known as "training data", in order to make
predictions or decisions without being explicitly programmed to do so. Machine learning
algorithms are used in a wide variety of applications, such as email filtering and computer
vision, where it is difficult or infeasible to develop conventional algorithms to perform the
needed tasks. Machine learning is closely related to computational statistics, which focuses on
making predictions using computers. The study of mathematical optimization delivers
methods, theory and application domains to the field of machine learning. Data mining is a
related field of study, focusing on exploratory data analysis through unsupervised learning. In
its application across business problems, machine learning is also referred to as predictive
analytics.
orientation in the new image are identified to filter out good matches.
In deep learning, a convolutional neural network (CNN, or ConvNet) isa class of deep,
feed-forward artificial neural networks, most commonly applied to analyzing visual imagery.
Each layer combines patches from previous layers. Convolutional Networks are
trainable multistage architectures composed of multiple stages Input and output of each stage
are sets of arrays called feature maps. At output, each feature maprepresents a particular feature
extracted at all locations on input. Each stage is composed of: a filter bank layer, a non-linearity
layer, and a feature pooling layer. A ConvNet is composed of 1, 2 or 3 such 3-layer stages,
followed by a classification module.
CHAPTER 7:
FLOW CHART
No
Yes
First the collection of data of faces with mask and without mask are done as shown in Figure 7.1.
The data is stored in the format of NumPy. The data with no masks are labelled as 0, similarly the
data with masks are labelled as 1. The data collection process ends.
The Mask detection process starts by accessing the camera, if the camera access is granted the face
detection process starts and creates a frame around the face. If the mask is detected it prints as
“mask” else it prints as “No mask” until the ESC key is pressed.
REFERENCES
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Classify COVID-19 Face Mask for Human Safety with YOLOv3," 2020 11th International
Conference on Computing, Communication and Networking Technologies (ICCCNT)
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