UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 10 Issue 3
March-2023
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2303616


Registration ID:
510754

Page Number

g99-g105

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Title

Real Time Object Detection for Effective Intelligent Transport System using Raspberry Pi

Abstract

Intelligent transport system (ITS) is not just limited to traffic congestion control and information, but also for road safety and efficient infrastructure usage. The problem related to traffic is a complex one requiring proper design and planning for developing a solution. For safety and harmony in the flow of traffic, drivers are expected to pay attention to identify, interpret and follow certain rules while driving. Misinterpretation of traffic signs and signals may lead to catastrophes. An automatic system in a car which detects, recognizes, interprets traffic signs, traffic signals and pedestrians effectively and gives warning to the driver would be of great help in reducing the misinterpretations and accidents. In the present study, traffic sign and signal recognition system has been developed to increase the safety of the road users by installing the developed hardware system inside the car which gives an audio output for alerting the driver. Tensor Flow algorithm was used for the real time object detection through deep learning due to its high accuracy. The algorithm is embedded into Raspberry Pi 4 prototype for processing and analysis to detect the traffic signs, signals and pedestrians from the real-time video which will be recorded by a camera placed in the system and produces an audio output to alert the driver. This work aims to study the accuracy, delay and reliability of the developed system using a Raspberry Pi 4 processor considering several scenarios related to the state of the situation and the condition of the traffic signs and movement of humans. A real-time tested hardware implementation has been conducted for different classes of traffic signs, traffic signals and pedestrians. The result showed more than 98% accuracy and is reliable with an acceptable delay.

Key Words

Intelligent Transport System, Traffic signs & traffic signals, Raspberry Pi, OpenCV, Tensorflow etc.

Cite This Article

"Real Time Object Detection for Effective Intelligent Transport System using Raspberry Pi", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.g99-g105, March-2023, Available :http://www.jetir.org/papers/JETIR2303616.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Real Time Object Detection for Effective Intelligent Transport System using Raspberry Pi", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppg99-g105, March-2023, Available at : http://www.jetir.org/papers/JETIR2303616.pdf

Publication Details

Published Paper ID: JETIR2303616
Registration ID: 510754
Published In: Volume 10 | Issue 3 | Year March-2023
DOI (Digital Object Identifier):
Page No: g99-g105
Country: chittoor, Andhra Pradesh, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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