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Recognition of Flat Type Signboard using Deep Learning
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딥러닝을 이용한 판류형 간판의 인식

논문 기본 정보

Type
Academic journal
Author
Kwon, Sang Il (Namseoul University) Kim, Eui Myoung (Namseoul University)
Journal
Korea Society of Surveying, Geodesy, Photogrammetry, and Cartography Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography Vol.37 No.4 KCI Accredited Journals SCOPUS
Published
2019.8
Pages
219 - 231 (13page)

Usage

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Topic
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Method
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Result
Recognition of Flat Type Signboard using Deep Learning
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Research history (4)

2020

Identifying Specifications of Flat Type Signboards Using a Stereo Camera
Kwon ,  Sang Il ,  Kim Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography 2020.02 Academic journal
Informatization of Signboards Using Deep Learning and a Stereo Camera : Development of Flat Type Signboards Recognition Technology Based on Image Processing
권상일 지리정보공학과 2020.01 Thesis

2019

Recognition of Flat Type Signboard using Deep Learning
Kwon ,  Sang Il ,  Kim Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography 2019.08 Academic journal
Recognition of Horizontal-flat Type Signboard Using Images
Sangil Kwon ,  Euimyoung Kim Proceedings of Korean Society for Geospatial Information Science 2019.05 Proceeding
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Abstract· Keywords

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The specifications of signboards are set for each type of signboards, but the shape and size of the signboard actually installed are not uniform. In addition, because the colors of the signboard are not defined, so various colors are applied to the signboard. Methods for recognizing signboards can be thought of as similar methods of recognizing road signs and license plates, but due to the nature of the signboards, there are limitations in that the signboards can not be recognized in a way similar to road signs and license plates. In this study, we proposed a methodology for recognizing plate-type signboards, which are the main targets of illegal and old signboards, and automatically extracting areas of signboards, using the deep learning-based Faster R-CNN algorithm. The process of recognizing flat type signboards through signboard images captured by using smartphone cameras is divided into two sequences. First, the type of signboard was recognized using deep learning to recognize flat type signboards in various types of signboard images, and the result showed an accuracy of about 71%. Next, when the boundary recognition algorithm for the signboards was applied to recognize the boundary area of the flat type signboard, the boundary of flat type signboard was recognized with an accuracy of 85%.
#Signboard Detection #Flat Type #Faster Region-Based Convolutional Neural Network #Watershed #K-Means Clustering #Boundary Area #간판 인식 #판류형 #Faster R-CNN #워터쉐드 #K-평균 군집화 #경계 영역

Contents

Abstract
초록
1. 서론
2. 간판의 유형 판별 및 영역 탐색
3. 간판 영상의 색상 분류
4. 간판의 경계선 추출
5. 사각형 검출
6. 영상 분할 기법을 이용한 간판 인식
7. 간판의 인식
8. 결론
References

References (14)

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Ahn, H.Y. and Lee, J.T. (2018), Classification of vehicles based on faster R-CNN suitable for use in actual road environments, Journal of Korean Institute of Intelligent Systems, Vol. 28, No. 3, pp. 210-218. (in Korean with English abstract) google schola Cha, S.H. and Kim, J.H. (2017), Analysis on managemental characteristics and problems of outdoor advertisement in Seoul metropolitan city, The Korean Society Of Design Culture, Vol. 23, No. 4, pp. 767-779. (in Korean with English abstract) google schola Choi, H.S. and Kim, E.M. (2015), Detection of road signs region and recognition of directional information, Proceedings of Korean Society for Geospatial Information Science, September 2015, Korea, pp. 197-198. google schola Claudio, R.J. and Rodrigo, S. (2004), Rectangle detection based on a windowed hough transform, Proceedings of the XVII Brasilian Symposium on Computer Graphics and Image Processing, October 2004, Brazil, pp. 113-120. google schola Hua, S. and Xiaoou, T. (2003), Generic sign board detection in images, MIR ‘03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval, 7 July 2003, California, USA, pp. 144-149. google schola

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