Concurrent Detection and Identification of Multiple Objects using YOLO Algorithm

The main purpose of this research paper is to study and report how the object detection and identification of multiple objects in an image or a video frameworks in real life. As part of this research, we have clearly presented about image classification, image localization, and image detection. The...

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Bibliographic Details
Published in:Symposium of Image, Signal Processing, and Artificial Vision pp. 1 - 6
Main Authors: Megalingam, Rajesh Kannan, Babu, Dasari Hema Teja Anirudh, Sriram, Ghali, YashwanthAvvari, Venkata Sai
Format: Conference Proceeding
Language:English
Published: IEEE 15.09.2021
Subjects:
anchor boxes
bounding boxes
CNN
Deep Neural Networks
image classification
image localization
Neural networks
Object detection
Planets
Real-time systems
Signal processing
Signal processing algorithms
Streaming media
YOLOv3
ISSN:2329-6259
Online Access:Get full text
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Summary:The main purpose of this research paper is to study and report how the object detection and identification of multiple objects in an image or a video frameworks in real life. As part of this research, we have clearly presented about image classification, image localization, and image detection. The entire work is carried out with the aid of YOLOv3 (You Only Look Once, Version 3) is an object detection algorithm which helps to detect real-time objects. YOLOv3 gives accurate results. It helps in declaring the localizers, classifiers, and detection. It uses a single neural network to the full image to detect the objects using bounding boxes thereby resulting in accurate outputs. The experiments and results show that YOLOv3 is extremely accurate. This rapidness in detecting objects is helpful in situations where humans can't detect with their naked eye cannot track multiple items simultaneously in an image or video frame.
ISSN:2329-6259
DOI:10.1109/STSIVA53688.2021.9592012