Blind Assistance System Using Machine Learning

EasyChair Preprint no. 7813

Abstract

Blindness is one of the most frequent and debilitating of the various disabilities. There are million visually impaired people in the globe, according to the World Health Organization (WHO). The proposed system is designed to aid visually impaired persons with real-time obstacle detection, avoidance, indoors and out navigation, and actual position tracking. The gadget proposed is a camera-visual detection hybrid that performs well in low light as part of the recommended technique, this method is utilized to detect and avoid impediments, as well as to aid visually impaired persons in identifying the environment around them. A simple and effective method for people with visual impairments to identify things in their environment and convert them into speech for improved comprehension and navigation. Along with these, we have depth estimation, which calculates the safe distance between the object and the person, allowing them to be more self-sufficient and less reliant on others. We were able to achieve this model with the help of TensorFlow and pre-trained models. The approach we suggest is dependable, inexpensive, practical, and practicable.

Keyphrases: depth estimation, object detection, Single Shot Detection, TensorFlow

@Booklet{EasyChair:7813,
  author = {Naveen Kumar and Sanjeevani Sharma and Ilin Mariam Abraham and Sathya Priya},
  title = {Blind Assistance System Using Machine Learning},
  howpublished = {EasyChair Preprint no. 7813},

  year = {EasyChair, 2022}}