Authors :
Prathamesh Sonawane; Rupa Gudur; Vedant Gaikwad; Harshad Jadhav
Volume/Issue :
Volume 8 - 2023, Issue 11 - November
Google Scholar :
https://tinyurl.com/572srfcr
Scribd :
https://tinyurl.com/yckw49rz
DOI :
https://doi.org/10.5281/zenodo.10109998
Abstract :
Live object recognition refers to the real-time
process of identifying and categorizing objects within a
given visual input, such as images. This technology
utilizes computer vision techniques and advanced
algorithms to detect objects, determine their dimension,
area and weight and often classify them into predefined
categories. Our system proposes R-CNN and YOLO to
determine the dimensions of the objects in real time.
YOLO takes a different approach by treating object
detection as a single regression problem. A single neural
network is trained to directly predict bounding boxes
and class probabilities for multiple objects in an image.
The input image is divided into a grid, and each grid cell
is responsible for predicting the objects whose center fall
within that cell. Live object recognition finds
applications in various fields, including autonomous
vehicles, surveillance systems, robotics, augmented
reality, and more. By providing instantaneous and
accurate insights into the surrounding environment, this
technology contributes to enhanced decision-making,
interaction, and automation across numerous domains.
The objects which can be recognized are solid objects
which we use daily such as electronic items, stationery
items, culinary items and many more. Our system “Live
Object Recognition using YOLO” is aimed to detect and
determine the objects and dimensions in real time.
Keywords :
Live Object Recognition, YOLO, Accuracy, Dimension Measure, CNN, Deep Learning, Convolution Neural Network, Object Detection, Machine Learning
Live object recognition refers to the real-time
process of identifying and categorizing objects within a
given visual input, such as images. This technology
utilizes computer vision techniques and advanced
algorithms to detect objects, determine their dimension,
area and weight and often classify them into predefined
categories. Our system proposes R-CNN and YOLO to
determine the dimensions of the objects in real time.
YOLO takes a different approach by treating object
detection as a single regression problem. A single neural
network is trained to directly predict bounding boxes
and class probabilities for multiple objects in an image.
The input image is divided into a grid, and each grid cell
is responsible for predicting the objects whose center fall
within that cell. Live object recognition finds
applications in various fields, including autonomous
vehicles, surveillance systems, robotics, augmented
reality, and more. By providing instantaneous and
accurate insights into the surrounding environment, this
technology contributes to enhanced decision-making,
interaction, and automation across numerous domains.
The objects which can be recognized are solid objects
which we use daily such as electronic items, stationery
items, culinary items and many more. Our system “Live
Object Recognition using YOLO” is aimed to detect and
determine the objects and dimensions in real time.
Keywords :
Live Object Recognition, YOLO, Accuracy, Dimension Measure, CNN, Deep Learning, Convolution Neural Network, Object Detection, Machine Learning
