Authors: Bhuvanesh ., Sreerambabu , Nimmy Pailochan, Kalidasan
DOI Link: https://doi.org/10.22214/ijraset.2023.55036
Certificate: View Certificate
According to the World Health Organization (WHO), there are millions of visually impaired individuals worldwide in detecting obstacles and identifying people. It emphasizes the advancements in information technology and spatial cognition theory for visually impaired individuals as a new opportunity. The prototype proposes a simple and cost-effective solution using artificial vision through an AI-based intelligent system. The system utilizes the Faster Region Convolutional Neural Network (FRCNN) architecture to recognize human and scene objects or obstacles in real-time, even in complex environments. It provides users with comprehensive information about the presence, position, and nature of targets, and uses voice messages to alert blind individuals about obstacles or people nearby. The goal is to create a user-friendly technology that facilitates communication and independence for visually impaired individuals, enabling them to navigate both indoor and outdoor locations effectively.
I. INTRODUCTION
“Visual impairment” is a broad term that is used to refer to any degree of vision loss that affects a person’s ability to perform the usual activities of daily life. The way in which vision impairments are classified differs across countries. The World Health Organization (the WHO) classifies visual impairment based on two factors: the visual acuity, or the clarity of vision, and the visual fields, which is the area from which you are able to perceive visual information, while your eyes are in a stationary position and you are looking straight at an object. Moving safely and efficiently through both indoor and outdoor environments can be challenging for the blind due to the unpredictability of obstacles. Traditional mobility aids, like white canes and guide dogs, remain valuable tools, but recent technological developments have significantly augmented these solutions.
Computer vision algorithms and depth-sensing technologies have been integrated into wearable devices to detect obstacles and offer real-time navigation assistance. These devices utilize cameras and sensors to create a spatial map of the surroundings, identifying potential obstacles like curbs, poles, staircases, and other hazards. Through auditory or haptic feedback, the user is alerted to the presence of these obstacles, allowing them to navigate more confidently and independently.
II. EXISTING SYSTEM
The existing systems and technologies for visually impaired navigation and assistance offer a diverse range of solutions. One approach involves a Kinect-based navigation system, requiring a backpack-carrying setup with a standard Kinect sensor, battery, and laptop. Utilizing SLAM technology, the smart cane provides centimeter-level accuracy for indoor positioning and obstacle detection. Another innovative system, the virtual haptic radar, replaces traditional sensors with a combination of 3D modeling and ultrasonic-based motion capture, warning users through vibrations when approaching objects. Mobile applications like Moovit, BlindSquare, and Lazzus leverage GPS and location databases to provide real-time guidance and information about points of interest. On the vision-based side, techniques like HOG and SSD enable object detection in images, adding another dimension to the array of solutions available. Together, these advanced technologies aim to enhance the mobility and independence of visually impaired individuals, enabling them to navigate their surroundings confidently.
Disadvantages of the existing navigation and assistance systems for visually impaired individuals include:
III. PROPOSED SYSTEM
The proposed system of the project is to design and fabricate a Smart Electronic Glass that designed to make recognizing faces and objects easier for visually impaired people.
IV. DEVELOPMENT ENVIRONMENT
A. Hardware Requirement
B. Software Requirement
V. MODULE DESCRIPTION
A. Blind Obstacle Firmware
This module involves designing an AI-powered smart glass firmware with an integrated camera.
Images captured by the camera are processed using FRCNN machine learning models, and the speech response is sent back to the user through the glass's built-in speaker.
B. Object Detection Phase
C. Face Recognition Phase
D. Prediction Module
E. Voice Integrator
Audio output, if a data is triggered during processing, voice synthesis is used to alert the user, generating for example: “stop” if there is an obstacle in the way. Saying that Hi Bhuvanesh.
VII. FUTURE ENHANCEMENT
In Conclusion, the proposed system is a comprehensive solution for assisting the visually impaired with known or unknown human identification and obstacle detection. It utilizes wearable technology, a CNN model, and voice assistance with Python Flask, TensorFlow, and MySQL. The CNN model is trained to provide real-time obstacle detection and human identification, with voice assistance through text-to-speech. Data management is handled using MySQL, allowing for customizable voice assistance settings. The user can access and manage the system through a user-friendly web interface. Overall, the system aims to enhance the independence and safety of visually impaired individuals, significantly improving their navigation and overall quality of life.
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Copyright © 2023 Bhuvanesh ., Sreerambabu , Nimmy Pailochan, Kalidasan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET55036
Publish Date : 2023-07-26
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here