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Traffic light detection using tensorflow object detection framework

Deep learning; Neural networks; Object recognition; Systems engineering; Convolutional neural network; Detection framework; Learning approach; Learning objects; Real-time problems; Robust detection; TensorFlow; Traffic light; Object detection

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Bibliographic Details
Main Authors: Janahiraman T.V., Subuhan M.S.M.
Other Authors: 57215350701
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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id my.uniten.dspace-24381
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spelling my.uniten.dspace-243812023-05-29T15:23:08Z Traffic light detection using tensorflow object detection framework Janahiraman T.V. Subuhan M.S.M. 57215350701 57215366072 Deep learning; Neural networks; Object recognition; Systems engineering; Convolutional neural network; Detection framework; Learning approach; Learning objects; Real-time problems; Robust detection; TensorFlow; Traffic light; Object detection Traditional methods in machine learning for detecting traffic lights and classification are replaced by the recent enhancements of deep learning object detection methods by success of building convolutional neural networks (CNN), which is a component of deep learning. This paper presents a deep learning approach for robust detection of traffic light by comparing two object detection models and by evaluating the flexibility of the TensorFlow Object Detection Framework to solve the real-time problems. They include Single Shot Multibox Detector (SSD) MobileNet V2 and Faster-RCNN. Our experimental study shows that Faster-RCNN delivers 97.015%, which outperformed SSD by 38.806% for a model which had been trained using 441 images. � 2019 IEEE. Final 2023-05-29T07:23:07Z 2023-05-29T07:23:07Z 2019 Conference Paper 10.1109/ICSEngT.2019.8906486 2-s2.0-85076434229 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076434229&doi=10.1109%2fICSEngT.2019.8906486&partnerID=40&md5=1640b5c16d36ac38a289b5c56ea3bb46 https://irepository.uniten.edu.my/handle/123456789/24381 8906486 108 113 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Deep learning; Neural networks; Object recognition; Systems engineering; Convolutional neural network; Detection framework; Learning approach; Learning objects; Real-time problems; Robust detection; TensorFlow; Traffic light; Object detection
author2 57215350701
author_facet 57215350701
Janahiraman T.V.
Subuhan M.S.M.
format Conference Paper
author Janahiraman T.V.
Subuhan M.S.M.
spellingShingle Janahiraman T.V.
Subuhan M.S.M.
Traffic light detection using tensorflow object detection framework
author_sort Janahiraman T.V.
title Traffic light detection using tensorflow object detection framework
title_short Traffic light detection using tensorflow object detection framework
title_full Traffic light detection using tensorflow object detection framework
title_fullStr Traffic light detection using tensorflow object detection framework
title_full_unstemmed Traffic light detection using tensorflow object detection framework
title_sort traffic light detection using tensorflow object detection framework
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806424125722329088
score 13.18916

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