research-article

Traffic Sign Detection for Panoramic Images Using Convolution Neural Network Technique

Authors:

Sathit Prasomphan,

Thanthip Tathong,

Primpisa CharoenprateepkitAuthors Info & Claims

HPCCT '19: Proceedings of the 2019 3rd High Performance Computing and Cluster Technologies Conference

Pages 128 - 133

https://doi.org/10.1145/3341069.3341090

Published: 22 June 2019 Publication History

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Abstract

This research presents a method for panoramic traffic sign images detection for regulatory signs and guide signs especially blue and green sign. A new approach for detecting the signs inside a large panoramic image was considered. A convolution neural network technique was used as tools for detecting. In addition, the steps required are the technique used in conjunction with the convolution neural network technique by using Tensorflow training to improve the accuracy of traffic sign detection. Moreover, to improve the accuracy of image detection, some image processing technique was added. For example, adding brightness to an image. From the experimental results, detection of traffic signs from panoramic images (360°) by using trained convolution neural network model to improve a traffic sign detection, the accuracy from panoramic images (360°) is better than the traditional model.

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Uikey RLone HAgarwal A(2024)Indian Traffic Sign Detection and Classification Through a Unified FrameworkIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.341111725:10(14866-14875)Online publication date: Oct-2024
https://doi.org/10.1109/TITS.2024.3411117
Kavitha CThamaraikannan VVigneshkumar MRaj B(2023)Traffic Sign Recognition and Voice-Activated Driving Assistance Using Raspberry Pi2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM)10.1109/ICONSTEM56934.2023.10142789(1-7)Online publication date: 6-Apr-2023
https://doi.org/10.1109/ICONSTEM56934.2023.10142789
Prasomphan SSuriyachay ESamonothai STamasri J(2023)Falling People Detection in Real Time Video Using Convolution Neural NetworkIntelligent Computing10.1007/978-3-031-37717-4_73(1127-1138)Online publication date: 1-Sep-2023
https://doi.org/10.1007/978-3-031-37717-4_73
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Index Terms

Traffic Sign Detection for Panoramic Images Using Convolution Neural Network Technique
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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HPCCT '19: Proceedings of the 2019 3rd High Performance Computing and Cluster Technologies Conference

June 2019

293 pages

ISBN:9781450371858

DOI:10.1145/3341069

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 22 June 2019

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HPCCT 2019: 2019 The 3rd High Performance Computing and Cluster Technologies Conference

June 22 - 24, 2019

Guangzhou, China

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Uikey RLone HAgarwal A(2024)Indian Traffic Sign Detection and Classification Through a Unified FrameworkIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.341111725:10(14866-14875)Online publication date: Oct-2024
https://doi.org/10.1109/TITS.2024.3411117
Kavitha CThamaraikannan VVigneshkumar MRaj B(2023)Traffic Sign Recognition and Voice-Activated Driving Assistance Using Raspberry Pi2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM)10.1109/ICONSTEM56934.2023.10142789(1-7)Online publication date: 6-Apr-2023
https://doi.org/10.1109/ICONSTEM56934.2023.10142789
Prasomphan SSuriyachay ESamonothai STamasri J(2023)Falling People Detection in Real Time Video Using Convolution Neural NetworkIntelligent Computing10.1007/978-3-031-37717-4_73(1127-1138)Online publication date: 1-Sep-2023
https://doi.org/10.1007/978-3-031-37717-4_73
Yan JZhao LDiao WWang HSun X(2021)AF-EMS Detector: Improve the Multi-Scale Detection Performance of the Anchor-Free DetectorRemote Sensing10.3390/rs1302016013:2(160)Online publication date: 6-Jan-2021
https://doi.org/10.3390/rs13020160
Santos AAbu POppus CReyes R(2020)Real-Time Traffic Sign Detection and Recognition System for Assistive DrivingAdvances in Science, Technology and Engineering Systems Journal10.25046/aj0504715:4(600-611)Online publication date: 2020
https://doi.org/10.25046/aj050471

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Automatic recognition algorithm of traffic signs based on convolution neural network

Korean sign language recognition based on image and convolution neural network

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