Authors :
Mahesh Pawaskar; Sahil Talathi; Shraddha Shinde; Digvijay Singh Deora; Adesh Hardas; Vrushali Devlekar
Volume/Issue :
Volume 8 - 2023, Issue 5 - May
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/2p85em2c
DOI :
https://doi.org/10.5281/zenodo.8049767
Abstract :
- Blind humans face many problems to interact
with their close by surroundings. The intention of this
paper is to offer a device to help blind humans to navigate
in addition to feel the barriers. We plan to propose an
operating model that is taking walks stick with inconstructed ultrasonic sensor with a micro controller
gadget. Detection and monitoring algorithms are laid out
in terms of extricating the capabilities of photographs and
motion pictures for protection and scrutiny applications.
famous algorithms of item detection consist of You only
look once (YOLO), area-based Convolutional Neural
Networks (RCNN), quicker RCNN (F-RCNN). RCNN has
higher accuracy in comparison to different algorithms,
but YOLO surpasses whilst pace is considered over
accuracy.
Keywords :
YOLOv4, Raspberry Pi, RCNN, Blind stick, Object Detection.
- Blind humans face many problems to interact
with their close by surroundings. The intention of this
paper is to offer a device to help blind humans to navigate
in addition to feel the barriers. We plan to propose an
operating model that is taking walks stick with inconstructed ultrasonic sensor with a micro controller
gadget. Detection and monitoring algorithms are laid out
in terms of extricating the capabilities of photographs and
motion pictures for protection and scrutiny applications.
famous algorithms of item detection consist of You only
look once (YOLO), area-based Convolutional Neural
Networks (RCNN), quicker RCNN (F-RCNN). RCNN has
higher accuracy in comparison to different algorithms,
but YOLO surpasses whilst pace is considered over
accuracy.
Keywords :
YOLOv4, Raspberry Pi, RCNN, Blind stick, Object Detection.
