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http://dx.doi.org/10.7472/jksii.2021.22.6.25

Sea Fog Level Estimation based on Maritime Digital Image for Protection of Aids to Navigation  

Ryu, Eun-Ji (Oceanic IT convergence Technology Research Center, Hoseo University)
Lee, Hyo-Chan (Smart Network Research Center, Korea Electronics Technology Institute)
Cho, Sung-Yoon (Smart Network Research Center, Korea Electronics Technology Institute)
Kwon, Ki-Won (Smart Network Research Center, Korea Electronics Technology Institute)
Im, Tae-Ho (Oceanic IT convergence Technology Research Center, Hoseo University)
Publication Information
Journal of Internet Computing and Services / v.22, no.6, 2021 , pp. 25-32 More about this Journal
Abstract
In line with future changes in the marine environment, Aids to Navigation has been used in various fields and their use is increasing. The term "Aids to Navigation" means an aid to navigation prescribed by Ordinance of the Ministry of Oceans and Fisheries which shows navigating ships the position and direction of the ships, position of obstacles, etc. through lights, shapes, colors, sound, radio waves, etc. Also now the use of Aids to Navigation is transforming into a means of identifying and recording the marine weather environment by mounting various sensors and cameras. However, Aids to Navigation are mainly lost due to collisions with ships, and in particular, safety accidents occur because of poor observation visibility due to sea fog. The inflow of sea fog poses risks to ports and sea transportation, and it is not easy to predict sea fog because of the large difference in the possibility of occurrence depending on time and region. In addition, it is difficult to manage individually due to the features of Aids to Navigation distributed throughout the sea. To solve this problem, this paper aims to identify the marine weather environment by estimating sea fog level approximately with images taken by cameras mounted on Aids to Navigation and to resolve safety accidents caused by weather. Instead of optical and temperature sensors that are difficult to install and expensive to measure sea fog level, sea fog level is measured through the use of general images of cameras mounted on Aids to Navigation. Furthermore, as a prior study for real-time sea fog level estimation in various seas, the sea fog level criteria are presented using the Haze Model and Dark Channel Prior. A specific threshold value is set in the image through Dark Channel Prior(DCP), and based on this, the number of pixels without sea fog is found in the entire image to estimate the sea fog level. Experimental results demonstrate the possibility of estimating the sea fog level using synthetic haze image dataset and real haze image dataset.
Keywords
Aids to Navigation; buoy; prevention of safety accidents; Haze(Fog) level; intensity estimation; image processing;
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  • Reference
1 K Nishino, L Kratz, S Lombardi. "Bayesian defogging", International journal of computer vision, Vol.98, No.3, pp.263-278, 2012. https://doi.org/10.1007/s11263-011-0508-1   DOI
2 J Janai, F Guney, A Behl, A Geiger, "Computer vision for autonomous vehicles: Problems, datasets and state of the art", Foundations and Trends® in Computer Graphics and Vision, Vol.12, No.1-3, pp.1-308, 2020. https://doi.org/10.1561/0600000079   DOI
3 K He, J Sun, X Tang, X., "Single image haze removal using dark channel prior", IEEE transactions on pattern analysis and machine intelligence, Vol.33, No.12, pp.2341-2353, 2010. https://doi.org/10.1109/TPAMI.2010.168   DOI
4 B Cai, X Xu, K Jia, C Qing, D Tao, "Dehazenet: An end-to-end system for single image haze removal", IEEE Transactions on Image Processing, Vol.25, No.11, pp.5187-5198, 2016. https://doi.org/10.1109/tip.2016.2598681   DOI
5 Y Xu, J Wen, L Fei, Z Zhang , "Review of video and image defogging algorithms and related studies on image restoration and enhancement", Ieee Access, Vol.4, pp.165-188, 2016. https://doi.org/10.1109/access.2015.2511558   DOI
6 R Fattal, "Single image dehazing", ACM transactions on graphics (TOG), Vol.27, No.3, pp.1-9, 2008. https://doi.org/10.1145/1399504.1360671   DOI
7 TW Bae, JH Han, KJ Kim, YT Kim, "Coastal Visibility Distance Estimation Using Dark Channel Prior and Distance Map Under Sea-Fog: Korean Peninsula Case", Sensors, Vol.19, No.20, pp.4432, 2019. https://doi.org/10.3390/s19204432   DOI
8 H Xu, G Zhai, X Wu, X Yang, "Generalized equalization model for image enhancement", IEEE Transactions on Multimedia, Vol.16, No.1, pp.68-82, 2014. https://doi.org/10.1109/tmm.2013.2283453   DOI
9 Z Ma, J Wen, C Zhang, Q Liu, D Yan, "An effective fusion defogging approach for single sea fog image", Neurocomputing, Vol.173, No.3, pp.1257-1267, 2016. https://doi.org/10.1016/j.neucom.2015.08.084   DOI
10 BS Moon, TG Kim "Study on Development of Social Cost Estimating Model for Aids to Navigation Accident(II)", Journal of Navigation and Port Research, Vol.43, No.3, pp.166-171, 2019. https://doi.org/10.5394/KINPR.2019.43.3.166   DOI
11 Ministry of Oceans and Fisheries, "2021 Aids to Navigation Implementation", 2020. https://www.mof.go.kr/jfile/readDownloadFile.do?fileId=MOF_ARTICLE_36927&fileSeq=1
12 Korea Coast Guard, "Statistics of maritime distress accidents by weather", 2020. https://www.index.go.kr/potal/main/EachDtlPageDetail.do?idx_cd=1621
13 YK Wang, CT Fan, "Single image defogging by multiscale depth fusion", IEEE Transactions on image processing, Vol.23, No.11, pp.4826-4837, 2014. https://doi.org/10.1109/tip.2014.2358076   DOI
14 HM Hu, Q Guo, J Zheng, H Wang, "Single image defogging based on illumination decomposition for visual maritime surveillance", IEEE Transactions on Image Processing, Vol.28, No.6, pp.2882-2897, 2019. https://doi.org/10.1109/tip.2019.2891901   DOI
15 A Palvanov, YI Cho, "Visnet: Deep convolutional neural networks for forecasting atmospheric visibility", Sensors, Vol.19, No.6, pp.1343, 2019. https://doi.org/10.3390/s19061343   DOI
16 J-P Tarel, N Hautiere, L Caraffa, A Cord, H Halmaoui, D Gruyer, "Vision Enhancement in Homogeneous and Heterogeneous Fog", in IEEE Intelligent Transportation Systems Magazine, Vol.4, No.2, pp.6-20, summer 2012. https://doi.org/10.1109/mits.2012.2189969   DOI
17 C Sakaridis, D Dai, L Van Gool, "Semantic foggy scene understanding with synthetic data", International Journal of Computer Vision, Vol.126, No.9, pp.973-992, 2018. https://doi.org/10.1007/s11263-018-1072-8   DOI
18 Valentin Valkov, "Sea Fog," Jan. 2020. https://youtu.be/7dwXGVkuTTg
19 S. Kim, G. Lee, Y. Ban and H. Lee, "Effectiveness Analysis on the Prevention of Marine Accidents of Aids to Navigation", Korea Maritime Institute, 2016. https://www.kmi.re.kr/eng/board/view.do?rbsIdx=95&idx=177
20 Buch Norbert, Sergio A. Velastin, and James Orwell, "A Review of computer vision techniques for the analysis of urban traffic", IEEE Transactions on intelligent transportation systems, Vol.12, No.3, pp.920-939, 2011. https://doi.org/10.1109/tits.2011.2119372   DOI