5530490
doi
10.35940/ijeat.F1379.089620
oai:zenodo.org:5530490
Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)
Publisher
Anagha R
Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India.
Arpitha S
Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India.
Sanjay B S
Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India.
Harshitha K
Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India.
Parking Assist using Convolution Neural Networks
Swasthi B S
Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India.
issn:2249-8958
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Convolution Neural Network (CNN),Reset, Transfer Learning, Feature extraction, Parking slot
<p>Parking vehicles are one of the most frustrating tasks that people face these days. Locating an available parking space is a huge headache especially in urban areas. This paper aims to design one such parking system which, in many ways reduces the hassles of parking. The paper presents a system where a Machine Learning model, Convolution Neural Network(CNN) is used to classify parking slots in a parking space into vacant and filled slots. In order to optimize the task of classification, the method of Transfer Learning is implemented in the paper. The problem of parking stands not only limited to causing inconvenience to the drivers, but also escalates to much larger and extensive problems, affecting a lot more people the environment. Hence it is very important to have a system is used parking system in place. The model proposed in the paper sends across parking information to a driver well in advance, there by greatly reducing the waiting time for the vehicle.</p>
Zenodo
2020-08-30
info:eu-repo/semantics/article
5530489
1632750504.006867
375823
md5:4481b77319c06ca5da75bf8c92106085
https://zenodo.org/records/5530490/files/F1379089620.pdf
public
2249-8958
Is cited by
issn
International Journal of Engineering and Advanced Technology (IJEAT)
9
6
248-252
2020-08-30