OPTIMIZED DEEP CNN BASED OBSTACLE DETECTION FOR AIDING VISUALLY IMPAIRED PERSONS

Abstract

A visually impaired person faces several challenges while they are moving towards unfamiliar environments. Hence, object
detection approaches provide a major solution for this issue. For that, various researchers have developed obstacle detection
approaches to help blind people however they have certain limitations. In this research, the optimized deep learning
techniques, named Social Optimization algorithm (SOA)-based Deep Convolutional Network (Deep CNN) is developed for
assisting visually damaged persons. For effective obstacle detection, the input videos are converted to multiple frames. In
feature extraction, relevant features, such as Convolutional Neural Network (CNN) features, Shape Local Binary Texture
(SLBT), and hierarchical skeleton features are extracted for further processing. Moreover, the object detection process is
carried out using Generative Adversarial Network (GAN). In addition, the object recognition process is done by Deep CNN
in which all the layers of Deep CNN are trained using SOA. In addition, the experimental result demonstrates that the
developed model attained the testing accuracy, mean average precision (mAP), and recall values of 0.9485, 0.9596, and
0.9735, correspondingly.