scholarly journals Real-Time Object Detection using Deep Learning and Open CV

2019 ◽  
Vol 8 (12S) ◽  
pp. 411-414
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Visually Impaired ◽  
Deep Neural Network ◽  
Raspberry Pi ◽  
Detailed Review ◽  
Real World Application ◽  
Satisfactory Performance ◽  
Visually Impaired People ◽  
Impaired People

The object detection is used in almost every realworld application such as autonomous traversal, visual system, face detection and even more. This paper aims at applying object detection technique to assist visually impaired people. It helps visually impaired people to know about the objects around them to enable them to walk free. A prototype has been implemented on a Raspberry PI 3 using OpenCV libraries, and satisfactory performance is achieved. In this paper, detailed review has been carried out on object detection using region – conventionaal neural network (RCNN) based learning systems for a real-world application. This paper explores the various process of detecting objects using various object detections methods and walks through detection including a deep neural network for SSD implemented using Caffe model.

Object Detection to Assist Visually Impaired People: A Deep Neural Network Adventure

2018 ◽  
pp. 500-510 ◽  
Author(s):  
Fereshteh S. Bashiri ◽  
Eric LaRose ◽  
Jonathan C. Badger ◽  
Roshan M. D’Souza ◽  
Zeyun Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Visually Impaired ◽  
Deep Neural Network ◽  
Visually Impaired People ◽  
Impaired People

Machine Intelligence to Assist Visually Impaired People: A Big Data Deep Neural Network Adventure

2018 ◽  
Author(s):  
Fereshteh S. Bashiri ◽  
Eric Larose ◽  
Jonathan Badger ◽  
Zeyun Yu ◽  
Peggy Peissig ◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Visually Impaired ◽  
Deep Neural Network ◽  
Machine Intelligence ◽  
Visually Impaired People ◽  
Impaired People

scholarly journals ML Algorithm-Based Virtual Eye Guidance System

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
LOKESHKUMAR G ◽  
RAMAPRAKASH G
Keyword(s):  
Object Detection ◽  
Guidance System ◽  
Raspberry Pi ◽  
Deaf People ◽  
Detection Algorithms ◽  
Real World Application ◽  
Visually Impaired People ◽  
Impaired People ◽  
The Many ◽  
Audio Output

Object detection is used in almost every real-world application, including autonomous traversal, visual systems, and facial recognition, to name a few. The purpose of this study is to apply object detection algorithms to assist visually impaired people. It allows vision impaired people to be aware of their surroundings, enabling them to move freely. With promising findings, a prototype was developed on a Raspberry PI 3 using OpenCV libraries. This research looks at the many methods of detecting items with audio output using various object detection algorithms, such as a deep neural network for SSD constructed using the Caffe model. Along with vocal coaching, we've incorporated an emergency button to inform those nearby and a vibrator to alert deaf people to the obstruction in front of the camera.

scholarly journals Object Detection and Recognition for Visually Impaired People

2020 ◽  
Vol 9 (4) ◽  
pp. 401-405
Keyword(s):  
Neural Network ◽  
Visually Impaired ◽  
Deep Neural Network ◽  
Sufficient Information ◽  
Blind People ◽  
The Real ◽  
Visually Impaired People ◽  
Impaired People ◽  
Familiar Environment ◽  
Audio Output

Good vision is an expensive gift but now a day’s loss of vision is becoming common issue. Blind or visually impaired people does not have any conscious about the danger they are facing in their daily life. To help the blind people the visual world has to be transformed into the audio world with the potential to inform them about objects. Various challenges are faced by visually impaired patients even in the familiar environment. Visually impaired individuals are at drawback due to lack of sufficient information about their familiar environment. This project employs a Convolution Neural Network for recognition of pre-trained objects. This project employs in deep learning a deep Neural Network (DNN) for recognizing the object which is captured from the real world. The captured image is compared with some pre trained objects that is stored in dataset .The comparative of the object is based on the shape and size of an objects. In deep neural network, TensorFlow package using a model called Mobile Net SSD that is comparing the real time capture image with pre trained object based on shape, size of the object. If the image is matched with that trained object, it will display the name of the object. Then the name of the object is converted into audio output with the help of gTTS. This will helps to identify and detect what object is present in front of blind people and give output as audio.

scholarly journals Visual Assistant for Blind People using Raspberry Pi

2021 ◽  
pp. 671-675
Author(s):  
Tejal Adep ◽  
Rutuja Nikam ◽  
Sayali Wanewe ◽  
Dr. Ketaki B. Naik
Keyword(s):  
Visually Impaired ◽  
Daily Life ◽  
Artificial Vision ◽  
Raspberry Pi ◽  
Blind People ◽  
Python Language ◽  
Visually Impaired People ◽  
Impaired People ◽  
Visual Aid ◽  
Impaired Person

Blind people face the problem in daily life. They can't even walk without any aid. Many times they rely on others for help. Several technologies for the assistance of visually impaired people have been developed. Among the various technologies being utilized to assist the blind, Computer Vision-based solutions are emerging as one of the most promising options due to their affordability and accessibility. This paper proposes a system for visually impaired people. The proposed system aims to create a wearable visual aid for visually impaired people in which speech commands are accepted by the user. Its functionality addresses the identification of objects and signboards. This will help the visually impaired person to manage day-to-day activities and navigate through his/her surroundings. Raspberry Pi is used to implement artificial vision using python language on the Open CV platform.

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