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http://dx.doi.org/10.7848/ksgpc.2019.37.3.119

Research on the Efficiency of Classification of Traffic Signs Using Transfer Learning  

Kim, June Seok (Department of GIS Engineering, Namseoul University)
Hong, Il Young (Department of Spatial Information Engineering, Namseoul University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.37, no.3, 2019 , pp. 119-127 More about this Journal
Abstract
In this study, we investigated the application of deep learning to the manufacturing process of traffic and road signs which are constituting the road layer in map production with 1 / 1,000 digital topographic map. Automated classification of road traffic sign images was carried out through construction of training data for images acquired by using transfer learning which is used in image classification of deep learning. As a result of the analysis, the signs of attention, regulation, direction and assistance were irregular due to various factors such as the quality of the photographed images and sign shape, but in the case of the guide sign, the accuracy was higher than 97%. In the digital mapping, it is expected that the automatic image classification method using transfer learning will increase the utilization in data acquisition and classification of various layers including traffic safety signs.
Keywords
Transfer Learning; Road Traffic Sign; Digital Mapping; Deep Learning; Classification;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., and Kudlur, M. (2016), Tensorflow: A system for largescale machine learning, In 12th Symposium on Operating Systems Design and Implementation, 2-4 November, Savannah, USA, pp. 265-283.
2 Famili, A., Shen, W. M., Weber, R., and Simoudis, E. (1997), Data preprocessing and intelligent data analysis, Intelligent Data Analysis, Vol. 1, No. 1, pp. 3-23.   DOI
3 Hong, I.Y. (2017), Land use classification using LBSN data and machine learning technique, Journal of the Korean Cartographic Association, Vol. 17, No. 3, pp. 59-67. (in Korean with English abstract)   DOI
4 Kim, J.Y. (2015), Introducing google tensorflow, Journal of the Korean Computer Information Society, Vol. 23, No. 2, pp. 9-15.   DOI
5 Lecun, Y., Bengio, Y., and Hinton, G. (2015), Deep learning, Nature, Vol. 521, No. 7553, 436.   DOI
6 Lee, C.H. and Hong, I.Y. (2017), Investigation of topographic characteristics of parcels using UAV and machine learning, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 35, No. 5, pp. 349-356.   DOI
7 Lee, J.G., Lee, T.H., and Yoon, S.R. (2014), Machine learning for big data analytics, Journal of Korean Telecommunications Association(Information and Communication), Vol. 31, No. 11, pp. 14-26.
8 Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., and Alsaadi, F.E. (2017), A survey of deep neural network architectures and their applications, Neurocomputing, Vol. 234, pp. 11-26.   DOI
9 Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., and Rabinovich, A. (2015), Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7-12 June, Boston, USA, pp. 1-9.
10 Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016), Rethinking the Inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 27-30 June, Las Vegas, USA, pp. 2818-2826.
11 Xia, X., Xu, C., and Nan, B. (2017), Inception-v3 for flower classification, In 2017 2nd International Conference on Image, Vision and Computing (ICIVC), 2-4 June, Chengdu, China, pp. 783-787.