Open Access
ARTICLE
Deep Learning Based Audio Assistive System for Visually Impaired People
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, 603203, India
* Corresponding Author: S. Kiruthika Devi. Email:
Computers, Materials & Continua 2022, 71(1), 1205-1219. https://doi.org/10.32604/cmc.2022.020827
Received 10 June 2021; Accepted 05 August 2021; Issue published 03 November 2021
Abstract
Vision impairment is a latent problem that affects numerous people across the globe. Technological advancements, particularly the rise of computer processing abilities like Deep Learning (DL) models and emergence of wearables pave a way for assisting visually-impaired persons. The models developed earlier specifically for visually-impaired people work effectually on single object detection in unconstrained environment. But, in real-time scenarios, these systems are inconsistent in providing effective guidance for visually-impaired people. In addition to object detection, extra information about the location of objects in the scene is essential for visually-impaired people. Keeping this in mind, the current research work presents an Efficient Object Detection Model with Audio Assistive System (EODM-AAS) using DL-based YOLO v3 model for visually-impaired people. The aim of the research article is to construct a model that can provide a detailed description of the objects around visually-impaired people. The presented model involves a DL-based YOLO v3 model for multi-label object detection. Besides, the presented model determines the position of object in the scene and finally generates an audio signal to notify the visually-impaired people. In order to validate the detection performance of the presented method, a detailed simulation analysis was conducted on four datasets. The simulation results established that the presented model produces effectual outcome over existing methods.Keywords
Cite This Article
S. Kiruthika Devi and C. N. Subalalitha, "Deep learning based audio assistive system for visually impaired people," Computers, Materials & Continua, vol. 71, no.1, pp. 1205–1219, 2022.