Raspberry PI based Food Recognition for Visually Impaired using YOLO Algorithm

2021 ◽  
Author(s):  
Analyn N. Yumang ◽  
Dave Emilson S. Banguilan ◽  
Clark Kent S. Veneracion
Keyword(s):  
Visually Impaired ◽  
Raspberry Pi ◽  
Food Recognition

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.

Mesh Network of eHealth Intelligent Agents for Visually Impaired and Blind People

2022 ◽  
pp. 240-271
Author(s):  
Dmytro Zubov
Keyword(s):  
Intelligent Agents ◽  
Visually Impaired ◽  
Assistive Devices ◽  
Mesh Network ◽  
Assistive Device ◽  
Raspberry Pi ◽  
Industrial Facilities ◽  
Blind And Visually Impaired ◽  
Wide Range ◽  
Human Faces

Smart assistive devices for blind and visually impaired (B&VI) people are of high interest today since wearable IoT hardware became available for a wide range of users. In the first project, the Raspberry Pi 3 B board measures a distance to the nearest obstacle via ultrasonic sensor HC-SR04 and recognizes human faces by Pi camera, OpenCV library, and Adam Geitgey module. Objects are found by Bluetooth devices of classes 1-3 and iBeacons. Intelligent eHealth agents cooperate with one another in a smart city mesh network via MQTT and BLE protocols. In the second project, B&VIs are supported to play golf. Golf flagsticks have sound marking devices with a buzzer, NodeMcu Lua ESP8266 ESP-12 WiFi board, and WiFi remote control. In the third project, an assistive device supports the orientation of B&VIs by measuring the distance to obstacles via Arduino Uno and HC-SR04. The distance is pronounced through headphones. In the fourth project, the soft-/hardware complex uses Raspberry Pi 3 B and Bytereal iBeacon fingerprinting to uniquely identify the B&VI location at industrial facilities.

scholarly journals Mobility Assistive Device for the Visually Impaired

2020 ◽  
Vol 6 (12) ◽  
pp. 129-133
Author(s):  
Puru Malhotra and Vinay Kumar Saini
Keyword(s):  
Real Time ◽  
Structural Design ◽  
Visually Impaired ◽  
Assistive Device ◽  
Raspberry Pi ◽  
Traditional Use ◽  
Smart Glasses ◽  
Visually Impaired People ◽  
Impaired People ◽  
Walking Stick

he paper is aimed at the design of a mobility assistive device to help the visually impaired. The traditional use of a walking stick proposes its own drawbacks and limitations. Our research is motivated by the inability of the visually impaired people to ambulate and we have made an attempt to restore their independence and reduce the trouble of carrying a stick around. We offer a hands-free wearable glass which finds it utility in real-time navigation. The design of the smart glasses includes the integration of various sensors with raspberry pi. The paper presents a detailed account of the various components and the structural design of the glasses. The novelty of our work lies in providing a complete pipeline for analysis of surroundings in real-time and hence a better solution for navigating during the day to day activities using audio instructions as output.

scholarly journals Smart Reader for Visually Impaired Using Raspberry Pi

2021 ◽  
Vol 1132 (1) ◽  
pp. 012032
Author(s):  
S Sarkar ◽  
G Pansare ◽  
B Patel ◽  
A Gupta ◽  
A Chauhan ◽  
...  
Keyword(s):  
Visually Impaired ◽  
Raspberry Pi

scholarly journals Low Cost Electronic Brailler

2020 ◽  
Vol 16 ◽  
Keyword(s):  
Visually Impaired ◽  
Third World ◽  
Electronic Communication ◽  
Low Cost ◽  
Raspberry Pi ◽  
The Third ◽  
Visually Impaired Students ◽  
Blind User ◽  
Teachers And Parents ◽  
The Third World

The majority of blind or visually impaired students in the third world countries are still using the mechanical brailler for their education. With technology advancements and electronic communication, relying on paper-based brailler would not be efficient nor productive. The "LCE Brailler" is a low-cost electronic brailler whose main features are to vocalize, braille, save and convert Braille characters typed by a blind student to alphabetical ones, which are then displayed on a computer’s monitor. In order to promote an interactive educational experience among students, teachers and parents, the proposed brailler has an affordable low price with advanced capabilities. The device’s design is simplistic and its keyboard is familiar to the blind user. It is based on the raspberry pi technology. The LCE device was tested by visually impaired students and proved to provide accurate mechanical functionality, accuracy, braille-to-text and text-to-audio blind assistant with a userfriendly graphical user interface.

scholarly journals Assistive guidance system for the visually impaired

2017 ◽  
Author(s):  
Rohit Takhar ◽  
Tushar Sharma ◽  
Udit Arora ◽  
Sohit Verma
Keyword(s):  
Credit Card ◽  
Visually Impaired ◽  
Low Cost ◽  
Histogram Equalization ◽  
Detection Algorithm ◽  
Contour Detection ◽  
Guidance System ◽  
Raspberry Pi ◽  
Pixel Intensity ◽  
Detection And Recognition

In recent years, with the improvement in imaging technology, the quality of small cameras have significantly improved. Coupled with the introduction of credit-card sized single-board computers such as Raspberry Pi, it is now possible to integrate a small camera with a wearable computer. This paper aims to develop a low cost product, using a webcam and Raspberry Pi, for visually-impaired people, which can assist them in detecting and recognising pedestrian crosswalks and staircases. There are two steps involved in detection and recognition of the obstacles i.e pedestrian crosswalks and staircases. In detection algorithm, we extract Haar features from the video frames and push these features to our Haar classifier. In recognition algorithm, we first convert the RGB image to HSV and apply histogram equalization to make the pixel intensity uniform. This is followed by image segmentation and contour detection. These detected contours are passed through a pre-processor which extracts the region of interests (ROI). We applied different statistical methods on these ROI to differentiate between staircases and pedestrian crosswalks. The detection and recognition results on our datasets demonstrate the effectiveness of our system.

scholarly journals Design and Implementation of Facial Recognition System for Visually Impaired using Image Processing

2019 ◽  
Vol 8 (4) ◽  
pp. 4803-4807
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Face Detection ◽  
Visually Impaired ◽  
Recognition System ◽  
Raspberry Pi ◽  
Design And Implementation ◽  
The Face ◽  
Visually Impaired Students ◽  
Facial Recognition System

One of the most difficult tasks faced by the visually impaired students is identification of people. The rise in the field of image processing and the development of algorithms such as the face detection algorithm, face recognition algorithm gives motivation to develop devices that can assist the visually impaired. In this research, we represent the design and implementation of a facial recognition system for the visually impaired by using image processing. The device developed consists of a programmed raspberry pi hardware. The data is fed into the device in the form of images. The images are preprocessed and then the input image captured is processed inside the raspberry pi module using KNN algorithm, The face is recognized and the name is fed into text to speech conversion module. The visually impaired student will easily recognize the person before him using the device. Experiment results show high face detection accuracy and promising face recognition accuracy in suitable conditions. The device is built in such a way to improve cognition, interaction and communication of visually impaired students in schools and colleges. This system eliminates the need of a bulk computer since it employs a handy device with high processing power and reduced costs.

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