AI- POWERED BASED BLIND AND VISUALLY IMPAIRED SYSTEM FOR SMART GLASS
S.R Sowmiya
Assistant Professor, Dept. of CSE Dhanalakshmi Srinivasan Engineering College (Autonomous)
Perambalur, Tamilnadu, India
sowmiya@dsengg.ac.in
Deepika S
UG Student, Dept. of CSE Dhanalakshmi Srinivasan Engineering College (Autonomous)
Perambalur, Tamilnadu, India
deepisiva2002@gmail.com
Elanthendral J
UG Student, Dept. of CSE Dhanalakshmi Srinivasan Engineering College (Autonomous)
Perambalur, Tamilnadu,
India
jai.elanthendral@gmail.com
Grithika R
UG Student, Dept. of CSE Dhanalakshmi Srinivasan Engineering College (Autonomous)
Perambalur, Tamilnadu,
India
rmgrithika2002@gmail..com
Jeevitha S.J
UG Student, Dept. of CSE Dhanalakshmi Srinivasan Engineering College (Autonomous)
Perambalur, Tamilnadu,
India jeevisathiya1102@gmail.com
Abstract— Visual impairment causes serious problems in daily life and mobility for millions of people worldwide. Against this background, smart assistive technology is gaining momentum to improve the independence and quality of life of the blind and visually impaired. They have difficulty in daily life because they cannot detect the problems around them, and one of their biggest problems is identifying people. Besides automation, there are many unexplored uses of search engines. This project includes an app that uses search to help blind people see things in front of them safely, and a facial recognition system with visual feedback that can help visually impaired people see familiar and unfamiliar people. The speaker will provide them with sound support. In this work, we use deep learning-based Regional Convolutional Neural Network (Faster R-CNN) to detect and identify people and objects in the environment. Faster local convolutional neural network technology processes and distributes images captured by cameras. The operator takes the image as input. Therefore, this model helps visually impaired people in a simpler way than a white cane.
Keywords—Visuallmpairment,Objectdetection,Faster Region Convolutional Neural Network.
