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.
Highlights
In recent times, Artificial Intelligence (AI) models started yielding better outcomes in terms of voice-rich virtual candidates like Siri and Alexa [1], independent vehicles (Tesla), robotics, and automated conversion (Google translator)
Computer vision methods are to be unified with Machine Learning (ML) model to provide moderate solutions for the above-defined problem
A computer vision module is projected to examine the currency with the help of Speeded-Up Robust Features (SURF) [2]
Summary
Artificial Intelligence (AI) models started yielding better outcomes in terms of voice-rich virtual candidates like Siri and Alexa [1], independent vehicles (Tesla), robotics (car manufacturing), and automated conversion (Google translator). Chen et al [3] presented a model to guide the visually-impaired people to analyze and go through the content. In this prediction model, the candidate regions are initially predicted with a text of special statistical features. Detecting the accurate location of objects is still a challenge In this background, the current research article presents an Efficient Object Detection Model with Audio Assistive System (EODM-AAS) using DL-based YOLO v3 model for visuallyimpaired people. Rest of the paper is organized as follows: Section 2 presents a review of state-of-the-art techniques for object detection and classification for assisting visually-impaired people.
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