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
Saved in:
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Conference Paper |
Published: |
Institute of Electrical and Electronics Engineers Inc.
2023
|
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.uniten.dspace-24381 |
---|---|
record_format |
dspace |
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 |