Traffic Congestion Detection Using Deep Learning

Dr. J. Bhuvana; Anusha C

Title:
Traffic Congestion Detection Using Deep Learning

Authors:
Anusha C | Dr. J. Bhuvana

Cite This Article :

Anusha C | Dr. J. Bhuvana "Traffic Congestion Detection Using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2, February 2022, pp.1195-1197, URL: https://www.ijtsrd.com/papers/ijtsrd49401.pdf

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Abstract :
Despite the huge amount of traffic surveillance videos and images have been accumulated in the daily monitoring, deep learning approaches have been underutilized in the application of traffic intelligent management and control. Traffic images, including various illumination, weather conditions, and vast scenarios are considered and preprocessed to set up a proper training dataset. In order to detect traffic congestion, a network structure is proposed based on residual learning to be pre-trained and fine-tuned. The network is then transferred to the traffic application and retrained with self-established training dataset to generate the Traffic Net. The accuracy of Traffic Net to classify congested and uncongested road states reaches 99% for the validation dataset and 95% for the testing dataset. The trained model is stored in cloud storage for easy access for application from anywhere. The proposed Traffic Net can be used by a regional detection of traffic congestion on a large-scale surveillance system. The effectiveness and efficiencies are magnificently demonstrated with quick detection in the high accuracy in the case study. The experimental trial could extend its successful application to traffic surveillance system and has potential enhancement for intelligent transport system in future.

Keywords :
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Publication Details:

Unique Identification Number : IJTSRD49401

Published In : Volume-6 | Issue-2, February 2022

Page Number(s) : 1195-1197

Publisher Name : IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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