This paper is published in Volume-7, Issue-3, 2021
Area
Deep Neural Network
Author
Sathishkumar M., Kavya N., Kowsalya P., Nandhini G, Kaviyadharshini S.
Org/Univ
Mahendra Institute of Technology, Namakkal, Tamil Nadu, India
Pub. Date
14 June, 2021
Paper ID
V7I3-1706
Publisher
Keywords
Vehicle Detection, DNN, Machine Learning, Neural Networks.

Citationsacebook

IEEE
Sathishkumar M., Kavya N., Kowsalya P., Nandhini G, Kaviyadharshini S.. Vehicle detection and classification using Deep Neural Networks, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Sathishkumar M., Kavya N., Kowsalya P., Nandhini G, Kaviyadharshini S. (2021). Vehicle detection and classification using Deep Neural Networks. International Journal of Advance Research, Ideas and Innovations in Technology, 7(3) www.IJARIIT.com.

MLA
Sathishkumar M., Kavya N., Kowsalya P., Nandhini G, Kaviyadharshini S.. "Vehicle detection and classification using Deep Neural Networks." International Journal of Advance Research, Ideas and Innovations in Technology 7.3 (2021). www.IJARIIT.com.

Abstract

Insightful transportation frameworks have recognized a proportion of consideration somewhat recently. In this space vehicle arrangement and restriction is the key errand. In this assignment the greatest test is to separate the highlights of various vehicles. Further, vehicle grouping and identification is a difficult issue to recognize and find in light of the fact that wide assortment of vehicles doesn’t follow the path discipline. In this article, to distinguish and find, we have made a convolution neural organization without any preparation to group and identify objects utilizing a cutting edge Deep neural organization dependent on quick locales. In this work we have considered three kinds of vehicles like transport, vehicle and bicycle for grouping and recognition. Our methodology will utilize the whole picture as information and make a bouncing box with likelihood evaluations of the element classes as yield. The aftereffects of the investigation have shown that the projected framework can significantly improve the exactness of the discovery..