Harita Üzerinde Sembol Tanima Için Test Otomasyonu

Fatmanur Turhan; Levent Çarkacioǧlu; Behçet Uǧur TöreyIn

doi:10.1109/SIU59756.2023.10223831

Harita Üzerinde Sembol Tanima Için Test Otomasyonu

Translated title of the contribution: Test Automation for Symbol Recognition on the Map

Fatmanur Turhan, Levent Çarkacioǧlu, Behçet Uǧur TöreyIn

Informatics Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this study, various machine learning and image analysis approaches such as Template Matching, HOG, SVM, Faster RCNN and YOLO are examined and compared for the symbol recognition problem in color maps. Some difficulties were identified regarding the forms of the symbols, the complexity of the maps or the placement of the symbols on the map. Observations about the success or failure of the methods against the difficulties defined according to the experiments are presented. It has been observed that methods involving artificial neural networks are more successful when performing symbol recognition on color maps. The highest result was obtained with Faster RCNN as 91%.

Translated title of the contribution	Test Automation for Symbol Recognition on the Map
Original language	Turkish
Title of host publication	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350343557
DOIs	https://doi.org/10.1109/SIU59756.2023.10223831
Publication status	Published - 2023
Event	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 - Istanbul, Turkey Duration: 5 Jul 2023 → 8 Jul 2023

Publication series

Name	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

Conference

Conference	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023
Country/Territory	Turkey
City	Istanbul
Period	5/07/23 → 8/07/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Access to Document

10.1109/SIU59756.2023.10223831

Cite this

Turhan, F., Çarkacioǧlu, L., & TöreyIn, B. U. (2023). Harita Üzerinde Sembol Tanima Için Test Otomasyonu. In 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 (31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU59756.2023.10223831

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title = "Harita {\"U}zerinde Sembol Tanima I{\c c}in Test Otomasyonu",

abstract = "In this study, various machine learning and image analysis approaches such as Template Matching, HOG, SVM, Faster RCNN and YOLO are examined and compared for the symbol recognition problem in color maps. Some difficulties were identified regarding the forms of the symbols, the complexity of the maps or the placement of the symbols on the map. Observations about the success or failure of the methods against the difficulties defined according to the experiments are presented. It has been observed that methods involving artificial neural networks are more successful when performing symbol recognition on color maps. The highest result was obtained with Faster RCNN as 91%.",

keywords = "Convolutional Neural Network, Feature Extraction, Object Detection, Software Testing, Support Vector Machines, Symbol Recognition, Template Matching",

author = "Fatmanur Turhan and Levent {\c C}arkacioǧlu and T{\"o}reyIn, {Beh{\c c}et Uǧur}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 ; Conference date: 05-07-2023 Through 08-07-2023",

year = "2023",

doi = "10.1109/SIU59756.2023.10223831",

language = "T{\"u}rk{\c c}e",

series = "31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Turhan, F, Çarkacioǧlu, L & TöreyIn, BU 2023, Harita Üzerinde Sembol Tanima Için Test Otomasyonu. in 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023. 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023, Institute of Electrical and Electronics Engineers Inc., 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023, Istanbul, Turkey, 5/07/23. https://doi.org/10.1109/SIU59756.2023.10223831

Harita Üzerinde Sembol Tanima Için Test Otomasyonu. / Turhan, Fatmanur; Çarkacioǧlu, Levent; TöreyIn, Behçet Uǧur.
31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Harita Üzerinde Sembol Tanima Için Test Otomasyonu

AU - Turhan, Fatmanur

AU - Çarkacioǧlu, Levent

AU - TöreyIn, Behçet Uǧur

PY - 2023

Y1 - 2023

N2 - In this study, various machine learning and image analysis approaches such as Template Matching, HOG, SVM, Faster RCNN and YOLO are examined and compared for the symbol recognition problem in color maps. Some difficulties were identified regarding the forms of the symbols, the complexity of the maps or the placement of the symbols on the map. Observations about the success or failure of the methods against the difficulties defined according to the experiments are presented. It has been observed that methods involving artificial neural networks are more successful when performing symbol recognition on color maps. The highest result was obtained with Faster RCNN as 91%.

AB - In this study, various machine learning and image analysis approaches such as Template Matching, HOG, SVM, Faster RCNN and YOLO are examined and compared for the symbol recognition problem in color maps. Some difficulties were identified regarding the forms of the symbols, the complexity of the maps or the placement of the symbols on the map. Observations about the success or failure of the methods against the difficulties defined according to the experiments are presented. It has been observed that methods involving artificial neural networks are more successful when performing symbol recognition on color maps. The highest result was obtained with Faster RCNN as 91%.

KW - Convolutional Neural Network

KW - Feature Extraction

KW - Object Detection

KW - Software Testing

KW - Support Vector Machines

KW - Symbol Recognition

KW - Template Matching

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U2 - 10.1109/SIU59756.2023.10223831

DO - 10.1109/SIU59756.2023.10223831

M3 - Konferans katkısı

AN - SCOPUS:85173432800

T3 - 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

BT - 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

Y2 - 5 July 2023 through 8 July 2023

ER -

Harita Üzerinde Sembol Tanima Için Test Otomasyonu

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