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Flood detection by using FCN-AlexNet
Author(s): Keum-Young Son; Mustafa Eren Yildirim; Jang-Sik Park; Jong-Kwan Song
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Paper Abstract

Floods are the natural disasters which can give serious damage to properties, roads, vehicles and even people. These damages bring huge payload both to individuals and governments. Thus, a system which can detect floods at early stage and warn the related offices immediately will be very useful for public. Detecting flooding early can save human lives, time, money for the government, as well as an important step to move towards smarter cities. In this paper, we propose the use of a deep learning architecture to detect floods in certain susceptible areas. We used FCN AlexNet deep learning architecture to train and test our dataset. Images of our dataset are collected from two PTZ cameras with different view angles. According to the experimental results, used system gets above 95% classification accuracy on both cameras.

Paper Details

Date Published: 15 March 2019
PDF: 5 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110412P (15 March 2019); doi: 10.1117/12.2523028
Show Author Affiliations
Keum-Young Son, Kyungsung Univ. (Korea, Republic of)
Mustafa Eren Yildirim, Bacheshir Univ. (Turkey)
Jang-Sik Park, Kyungsung Univ. (Korea, Republic of)
Jong-Kwan Song, Kyungsung Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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