TRAFFIC SIGN CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK.
Traffic signs are a very crucial part in ensuring the safety of people travelling on road and in avoiding accidents. Often, we find various types of signs on the roadsides like speed limit signs, stop signs, yield signs etc. As humans, we can recognize these signs by looking at them, but computer sy...
| Publicado en: | Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 2987 - 2993 |
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| Autores principales: | , , , |
| Formato: | pictorial tables/charts Journal Article |
| Publicado: |
Turkish Journal of Physiotherapy & Rehabilitation
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151006320&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151006320 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13008757 YU1 jtl: Turkish Journal of Physiotherapy Rehabilitation issn: 13008757 maglogo: N pubinfo: dt: 2021 vid: 32 iid: 2 pid: 20392 pub: Turkish Journal of Physiotherapy & Rehabilitation place: Kizilay/ Ankara, <Blank> artinfo: ui: 151006320 151006320 151006320 151006320 ppf: 2987 ppct: 6 formats: fmt: @attributes: type: P tig: atl: TRAFFIC SIGN CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK. aug: au: RACHAPUDI, VENUBABU RAM, M. SITHA DILEEP, P. ROY, T. L. DEEPIKA affil: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India sug: subj: Neural Networks (Computer) Signage Classification Transportation ab: Traffic signs are a very crucial part in ensuring the safety of people travelling on road and in avoiding accidents. Often, we find various types of signs on the roadsides like speed limit signs, stop signs, yield signs etc. As humans, we can recognize these signs by looking at them, but computer systems cannot interpret what they mean. Traffic sign classification project aims at classifying these traffic signs automatically into their respective types using Convolutional neural networks. This project can then be implemented in autonomous driving vehicles, helping them in making better decisions and providing safer drives. In this project, we use the German Traffic sign dataset which comprises of traffic sign images of forty-three classes. Along with convolutional neural networks, we also use various preprocessing techniques like data augmentation, grayscaling, normalization for enhancing the feature recognition aspect of images. pubtype: Academic Journal doctype: pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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