ML for blind people.pptx

Department of Electronics Engineering
Department of Electronics Engineering, VESIT 2022- 23
Sr No Group Member Roll No
1. Sejal Bishoyi 11
2. Jaykumar Kabra 26
3. Swaraj Patil 44
4. Om Suwarnakar 62
THIRD EYE
Name of Guide: Mr. Abhijeet Shete
D11A Group No: G11-7

1. Today, machine learning is used in many types of industries from medical image
processing to autonomous cars. Detecting objects in images has also become one of the
important research areas and now computers are able to not only detect objects but also
are able to draw bounding boxes on it. This is also known as computer vision.
2. Therefore , we proposed the implementation of computer vision machine learning
ssssalgorithms to detect objects.
Why This Project?

Why This Project?
The World Health Organization (WHO) reported that at least 2.2 billion people
worldwide have a visual impairment or blindness
1. 1.09 billion people, over the age of 35, suffer from visual impairment .
1. Assistive devices have been used for the blind and visually impaired people to
overcome various physical, social, infrastructural, and accessibility barriers to
independence and to live active, productive, and independent lives as equal
members of the society

Introduction
1. Vision is one of the very essential human senses and it plays the most important role in
human perception about the surrounding environment.
2. Detecting objects in images has also become one of the important research areas and now
computers are able to not only detect objects but also are able to draw bounding boxes on
it. This is also known as computer vision.
3. We proposed the implementation of computer vision machine learning algorithms for
object detection.

Literature Review
1.2019 6th IEEE International Conference on Engineering Technologies
and Applied Sciences (ICETAS)
Object Detection and Narrator for Visually Impaired People.
This paper explains how convolution neural networks are
trained on ImageNet dataset that can detect objects and
narrate detected objects information to the visually impairs
person. This implementation can be used with any device
using a camera that includes computers, tablets and mobile
phones.

Literature Review
2.International Journal of Engineering Research & Technology (IJERT)
Vol. 9 Issue 09, September-2020
Assistive Object Recognition System for Visually Impaired
This paper proposed to aid the visually impaired by introducing a system that is most
feasible, compact, and cost-effective. So, the paper
implied a system that makes use of Raspberry Pi in which
you only look once (YOLO v3) machine learning
algorithm trained on the coco database is applied.

Literature Review
3.2020 IEEE Region 10 Symposium (TENSYMP), 5-7 June 2020, Dhaka,
Bangladesh
Assistive Technology for Visually Impaired using Tensorflow Object in
Raspberry Pi and Coral USB Accelerator
This paper aims to develop an assistive technology
based on Computer Vision, Machine Learning and
Tensorflow to support visually impaired people.
The proposed system will allow the users to
navigate independently using real-time object detection and identification.

Literature Review
4.European Journal of Molecular & Clinical Medicine ISSN 2515-8260
Volume 7, Issue 4, 2020
Real Time Object Detector for Visually Impaired using OPEN
The goal of the present project is to model an object detector to detect objects for visually
impaired people and other commercial purposes by
recognizing the objects at a particular distance.This paper
propose a computer vision concept to convert objects to
text by importing the pre-trained dataset model from the
caffemodel framework and the texts are further converted
into speech.

Hardware/Software
Requirements
Hardware:
Integrated Camera
Integrated Speaker
Software:
MATLAB & Simulink
Libraries:
1. Computer Vision Toolbox
2. Computer vision Toolbox Model for
Mask R-CNN Instance Segmentation
3. Deep Learning Toolbox
4. Deep Learning Toolbox For ResNet-50
Network
5. Image Processing Toolbox
6. MATLAB Support Package For USB
Webcams

Department of Electronics Engineering, VESIT 2022 -23
Flowchart

Block Diagram

Working
1. Initially,the images are captured through camera and these images are send to the
pretrained model.
2. A machine learning model (resnet50-coco) detect the image and find the objects in
the image .
3. At the backend,image processing takes place where necessary processes such as
features detection,features extraction ,etc. takes place.
4. Once the object is detected,the output is send to the user in the form of audio.

Courses
Matlab Onramp
Modules - 14
Status - Completed
Matlab Machine Learning
Modules - 6
Status - Completed
Matlab Deep learning
Modules - 13
Status-Completed
Matlab Image Processing
Modules - 11
Status - Completed

Plan of Implementation
MONTH PLAN
August 2022 Deciding the topic and researching
September 2022 Completing the courses required for the project
October 2022 Adding the image data and image processing
November 2022 Completion of the project (Software)

Applications
1. Tracking objects :
2. People Counting :
3. Automated CCTV :

Applications
4. Person Detection :
4. Vehicle Detection :

Result

Result
Accuracy Graph Confusion Matrix

Conclusion
In this paper we proposed a system which can be used to detect objects in various fields
like medical science , automobile industries and can even be used to assist the visually
impaired person in understanding the environment by narrating the objects in the
surrounding. The developed system is based on using a MATLAB which on loading takes
the image from the camera and pass that image to the server. On server side, a trained
machine learning model is deployed to detect the objects in that image. The result of
detection is passed to the client where a voice library narrates the results to visually
impaired person.

References
1. World Health Organization Visual Impairment and Blindness. [(accessed on 24 January 2016)]. Available online:
http://www.Awho.int/mediacentre/factsheets/fs282/en/
2. American Foundation for the Blind. [(accessed on 24 January 2016)]. Available online: http://www.afb.org/
3. National Federation of the Blind. [(accessed on 24 January 2016)]. Available online: http://www.nfb.org/
4. Velázquez R. Wearable and Autonomous Biomedical Devices and Systems for Smart Environment. Springer; Berlin/Heidelberg, Germany:
2010. Wearable assistive devices for the blind; pp. 331–349. [Google Scholar]
5. Baldwin D. Wayfinding technology: A road map to the future. J. Vis. Impair. Blind. 2003;97:612–620. [Google Scholar]
6. Blasch B.B., Wiener W.R., Welsh R.L. Foundations of Orientation and Mobility. 2nd ed. AFB Press; New York, NY, USA: 1997. [Google
Scholar]
7. Shah C., Bouzit M., Youssef M., Vasquez L. Evaluation of RUNetra tactile feedback navigation system for the visually-impaired; Proceedings
of the International Workshop on Virtual Rehabilitation; New York, NY, USA. 29–30 August 2006; pp. 72–77. [Google Scholar]
8. Hersh M.A. International Encyclopedia of Rehabilitation. CIRRIE; Buffalo, NY, USA: 2010. The Design and Evaluation of Assistive Technology
Products and Devices Part 1: Design. [Google Scholar]
9.who.int
…

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ML for blind people.pptx

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ML for blind people.pptx