dc.contributor.advisor | Hossain Bhuian, Dr. Mohammed Belal | |
dc.contributor.author | Chowdhury, Faizul Bari | |
dc.contributor.author | Faruqui, Tahmid Azim | |
dc.contributor.author | Razzaque, Rylah Marzia | |
dc.contributor.author | Iqbal, Nayeem | |
dc.date.accessioned | 2024-01-16T06:45:39Z | |
dc.date.available | 2024-01-16T06:45:39Z | |
dc.date.copyright | 2023 | |
dc.date.issued | 2023-01 | |
dc.identifier.other | ID: 16321168 | |
dc.identifier.other | ID: 17121072 | |
dc.identifier.other | ID: 17121079 | |
dc.identifier.other | ID: 181221047 | |
dc.identifier.uri | http://hdl.handle.net/10361/22162 | |
dc.description | This final year design project is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2023. | en_US |
dc.description | Cataloged from PDF version of final year design project. | |
dc.description | Includes bibliographical references (pages 74-79). | |
dc.description.abstract | Intelligent traffic system is not new to this modern world in order to mitigate traffic congestion.
However, a proper management plan for a four way intersection lacks in our cities. An
enhanced and optimized system is introduced in our project which in measure the density of
all the vehicles plying on the road. The project is conducted on the Dhaka city where live traffic
data has been collected from Shoinik Club Mor. A deep learning system is conducted where
optimized YOLOv5 algorithm has been used to count the vehicles and the datasets for different
vehicles are collected from COCO datasets and some datasets from BRTA (Bangladesh Road
Transport Authority). After analyzing in different traffic situation for over 1260 frames at a
rate of 30 FPS, we get around 88% accuracy in terms of detection. This operation sets the initial
time for green signal as 16 seconds and then it changes according to the density occupied by
vehicles of a particular road. | en_US |
dc.description.statementofresponsibility | Faizul Bari Chowdhury | |
dc.description.statementofresponsibility | Tahmid Azim Faruqui | |
dc.description.statementofresponsibility | Rylah Marzia Razzaque | |
dc.description.statementofresponsibility | Nayeem Iqbal | |
dc.format.extent | 91 pages | |
dc.language.iso | en | en_US |
dc.publisher | Brac University | en_US |
dc.rights | Brac University project reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. | |
dc.subject | Artificial intelligence | en_US |
dc.subject | Deep learning | en_US |
dc.subject | YOLOv5 | en_US |
dc.subject | Datasets | en_US |
dc.subject | IoU | en_US |
dc.subject | Non max spression | en_US |
dc.subject.lcsh | Image processing -- Digital techniques. | |
dc.subject.lcsh | Cognitive learning theory (Deep learning) | |
dc.title | Density based traffic control system for a four way intersection | en_US |
dc.type | Project report | en_US |
dc.contributor.department | Department of Electrical and Electronic Engineering, Brac University | |
dc.description.degree | B. Electrical and Electronic Engineering | |