Using Convolution Neural Network for flooding detection in CCTV images in urban areas

被引：0
|
作者：
Su, Yaun-Fong [1 ]
Chien, Ta-Chun [2 ]
Lin, Yi-Ting [2 ]
机构：
[1] National Taiwan Ocean University, Department of Harbor and River Engineering, Taiwan
[2] National Taiwan Ocean University, Geographic Information System Research Center, Taiwan
来源：
Journal of Taiwan Agricultural Engineering | 2024年 / 70卷 / 02期
关键词：
Convolution neural network (CNN) has been proved to be very useful in many research fields. However, there are still many possible applications in disaster monitoring and emergency operations. In Taiwan, CCTV also plays an important role for providing real-time situation in the field. Due to its importance, there are more than 20,000 CCTV working in Taiwan to provide real-time information. During flooding events, however, operation agency still needs manpower to check every CCTV footage and it is time and labor consuming. To address this issue, we collected 52,941 CCTV images during typhoon and storm events in 2023. From these images, we selected 3,540 images within urban area during daytime as the training dataset of the CNN model for flooding detection in CCTV image in urban areas. The developed CNN model has 2.4 million parameters to train and the training time costs about 1 hour. The trained CNN model shows a good performance with overall accuracy reaching 98 % while the accuracy for flooding detection for training and testing datasets reaches 85 %. This model may be further improved by considering two-stages CNN model and hopefully may make flooding detection using CCTV in a more efficient way. © 2024, Taiwan Agricultural Engineers Society. All rights reserved;
D O I：
10.29974/JTAE.202406_70(2).0003
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：30 / 39
相关论文
共 50 条
  • [1] Vehicle Detection in Remote Sensing Images of Dense Areas Based on Deformable Convolution Neural Network
    Gao Xin
    Li Hui
    Zhang Yi
    Yan Menglong
    Zhang Zongshuo
    Sun Xian
    Sun Hao
    Yu Hongfeng
    JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY, 2018, 40 (12) : 2812 - 2819
  • [2] A neural network based intelligent intruders detection and tracking system using CCTV images
    Fung, CC
    Jerrat, N
    IEEE 2000 TENCON PROCEEDINGS, VOLS I-III: INTELLIGENT SYSTEMS AND TECHNOLOGIES FOR THE NEW MILLENNIUM, 2000, : A409 - A414
  • [3] Automatic Microaneurysms Detection on Retinal Images Using Deep Convolution Neural Network
    Hatanaka, Yuji
    Ogohara, Kazunori
    Sunayama, Wataru
    Miyashita, Mitsuhiro
    Muramatsu, Chisako
    Fujita, Hiroshi
    2018 INTERNATIONAL WORKSHOP ON ADVANCED IMAGE TECHNOLOGY (IWAIT), 2018,
  • [4] Pulmonary nodule detection based on CT images using Convolution neural network
    Jin, Xin-Yu
    Zhang, Yu-Chen
    Jin, Qi-Liang
    PROCEEDINGS OF 2016 9TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 1, 2016, : 202 - 204
  • [5] Human Gender Detection from Facial Images Using Convolution Neural Network
    Sumi, Tahmina Akter
    Hossain, Mohammad Shahadat
    Ul Islam, Raihan
    Andersson, Karl
    APPLIED INTELLIGENCE AND INFORMATICS, AII 2021, 2021, 1435 : 188 - 203
  • [6] Traffic Congestion Detection: Learning from CCTV Monitoring Images using Convolutional Neural Network
    Kurniawan, Jason
    Syahra, Sensa G. S.
    Dewa, Chandra K.
    Afiahayati
    INNS CONFERENCE ON BIG DATA AND DEEP LEARNING, 2018, 144 : 291 - 297
  • [7] Dysarthria detection using convolution neural network
    Mahendran M.
    Visalakshi R.
    Balaji S.
    Measurement: Sensors, 2023, 30
  • [8] Dangerous Objects Detection of X-Ray Images Using Convolution Neural Network
    Zou, Lekang
    Yusuke, Tanaka
    Hitoshi, Iba
    SECURITY WITH INTELLIGENT COMPUTING AND BIG-DATA SERVICES, 2020, 895 : 714 - 728
  • [9] Brain Tumor Detection Based on Amended Convolution Neural Network Using MRI Images
    Mohanasundari, M.
    Chandrasekaran, V
    Anitha, S.
    KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2023, 17 (10): : 2788 - 2808
  • [10] Crack Detection and Classification in Asphalt Pavement Images using Deep Convolution Neural Network
    Yusof, N. A. M.
    Osman, M. K.
    Noor, M. H. M.
    Ibrahim, A.
    Tahir, N. M.
    Yusof, N. M.
    2018 8TH IEEE INTERNATIONAL CONFERENCE ON CONTROL SYSTEM, COMPUTING AND ENGINEERING (ICCSCE 2018), 2018, : 227 - 232
12345