| Issue |
ITM Web Conf.
Volume 57, 2023
Fifth International Conference on Advances in Electrical and Computer Technologies 2023 (ICAECT 2023)
|
|
|---|---|---|
| Article Number | 01014 | |
| Number of page(s) | 12 | |
| Section | Software Engineering & Information Technology | |
| DOI | https://doi.org/10.1051/itmconf/20235701014 | |
| Published online | 10 November 2023 | |
Object Detection and Localization for Visually Impaired
Department of CSE, Nitte Meenakshi Institute of technology, Bangalore, Karnataka, INDIA
dileep.bolla@gmail.com
hrishitarauniyar123.np@gmail.com
sowmyadnrk@gmail.com
1nt19cs151rakshitha@nmit.ac.in
nabirasoolshaik43@gmail.com
Now a days Both temporary and permanent disabilities affect a large number of people in this one of the disabilities is blindness there are a lot of blind persons in the world. Nearly 390 lakh individuals are fully blind, and another 2850 lakh are purblind, or visually impaired, according to the World Health Organisation (WHO). Many supporting or guiding systems have been established, and are still being developed, to improve people’s daily lives as they move from one place to another. Therefore, the main concept behind our suggested method is to provide an auto-assistance system for people who are visually impaired. As a result of their inability to see the object, the disabled person may find this auto-assistance system useful. To create an assisting system for blind persons, numerous methods have been put into place. Some systems are still being studied. The implemented models had a number of drawbacks when it came to object detection. We suggest a new method that uses CNNs (Convolutional Neural Networks) to aid those who are blind or visually impaired.
Key words: CNN (Convolution Neural Network) / YOLO / object detection / localization / Deep Learning
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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