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Object Identification Using Mobile Device for Visually Impaired Person

Akarapu, Deepika
http://orcid.org/0000-0001-7975-4312
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1628092619349812

Abstract Details

2021, Master of Computer Science (M.C.S.), University of Dayton, Computer Science.
The human eye perceives up to 80% of all the impressions and acts as the best shield from threat. While it is believed and accepted that vision is a predominant sense in people, as per the World Health Organization, around 40 million individuals on the planet are blind, and 250 million have some type of visual disability. As a result, a lot of research and papers are being suggested to create accurate and efficient navigation models utilizing computer vision and deep learning approaches. These models should be fast and efficient, and they should be able to run on low-power mobile devices to provide real-time outdoor assistance. Our objective is to extract and categorize the information from the live stream and provide audio feedback to the user within the University campus. The classification of the objects in the stream is done by a CNN model and sent as an input for the voice feedback, which is divided into several frames using the OpenCV library and converted to audio information for the user in the real-time environment using the Google text to speech module. The results generated by the CNN model for image classification have an accuracy of over 95 percent, and real-time audio conversion is a rapid transition technique, resulting in an algorithm that performs competing with other prior state-of-art methods. We also want to integrate the application in smartphones, into our mobile app to provide a more user-friendly experience for the end-users.
Dr. Mehdi R. Zargham (Advisor)
Computer Science
Classification; CNN (Convolutional Neural Network); Visually Impaired; Voice Alert; Mobile Device; University Premises

Recommended Citations

Citations

  • Akarapu, D. (2021). Object Identification Using Mobile Device for Visually Impaired Person [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1628092619349812

    APA Style (7th edition)

  • Akarapu, Deepika. Object Identification Using Mobile Device for Visually Impaired Person. 2021. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1628092619349812.

    MLA Style (8th edition)

  • Akarapu, Deepika. "Object Identification Using Mobile Device for Visually Impaired Person." Master's thesis, University of Dayton, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1628092619349812

    Chicago Manual of Style (17th edition)

dayton1628092619349812
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This open access ETD is published by University of Dayton and OhioLINK.