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Traffic light detection using tensorflow object detection framework

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dc.citedby20
dc.contributor.authorJanahiraman T.V.en_US
dc.contributor.authorSubuhan M.S.M.en_US
dc.contributor.authorid57215350701en_US
dc.contributor.authorid57215366072en_US
dc.date.accessioned2023-05-29T07:23:07Z
dc.date.available2023-05-29T07:23:07Z
dc.date.issued2019
dc.descriptionDeep 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 detectionen_US
dc.description.abstractTraditional 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8906486
dc.identifier.doi10.1109/ICSEngT.2019.8906486
dc.identifier.epage113
dc.identifier.scopus2-s2.0-85076434229
dc.identifier.spage108
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85076434229&doi=10.1109%2fICSEngT.2019.8906486&partnerID=40&md5=1640b5c16d36ac38a289b5c56ea3bb46
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24381
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2019 IEEE 9th International Conference on System Engineering and Technology, ICSET 2019 - Proceeding
dc.titleTraffic light detection using tensorflow object detection frameworken_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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