AUTO-ASSISTANCE SYSTEM FOR VISUALLY IMPAIRED PEOPLE USING DEEP LEARNING

World Health Organization (WHO) outlined that there are two eighty-five million visually disabled worldwide. Among them thirty-nine million people are completely blind. One of the difficult activities that could be conducted by visually impaired is object detection which could be implemented using Machine Learning. It is an approach towards Artificial Intelligence that provides system the capacity for natural learning and development from experience without specifically programmed. It provides computer vision to the system which make decisions based on training algorithms. The chief goal of this research paper is to develop an object detection system to assist totally blind individual to manage their activities independently. Paper also compares different object detection algorithms like Haar Cascade and Convolutional Neural Network with yolov5(CNN). Haar Cascade classifier is a basic face detection algorithm which could also be trained to detect different objects whereas convolutional neural network falls under deep learning approach which could be employed for object recognition. The custom dataset is created with 2300 images consisting of 3 different classes. This comparison is being executed to find the yolov5 as a suitable algorithm for this system from the aspect of accuracy for real time scenario.