Deep Neural Networks based Object Detection by Single Shot Detector Model

  • Nagaraj Bhat, Madhusudan Kulkarni,Santosh M Herur

Abstract

Automated systems need dependable, meticulous detection as well as recognition of surrounding
objects in real-time surroundings. This paper describes the usage of methods of deep learning using
convolutional type of Neural Networks intended for computer vision applications. This study goes one
step further and tackles the issue of object detection by Deep Neural Networks, the strategy is not just
based on segregating classes but additionally localize the different objects to various classes
precisely. The work aims at providing observations and conclusions primarily based on a real-time
object detection approach trained by the use of single Shot detector(SSD) model. With addition,
evaluating the SSD model together with other popular models like Faster R-CNN is carried out and
inferences show that SSD is a much better real-time model.

Published
2020-05-20
How to Cite
Nagaraj Bhat, Madhusudan Kulkarni,Santosh M Herur. (2020). Deep Neural Networks based Object Detection by Single Shot Detector Model. International Journal of Advanced Science and Technology, 29(7), 2505-2515. Retrieved from http://sersc.org/journals/index.php/IJAST/article/view/18015
Section
Articles