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