{"id":1813,"date":"2025-05-02T20:08:47","date_gmt":"2025-05-02T19:08:47","guid":{"rendered":"http:\/\/newskbncran.ru\/?page_id=1813"},"modified":"2026-06-03T15:45:44","modified_gmt":"2026-06-03T14:45:44","slug":"27-1-15-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/27-1-15-en\/","title":{"rendered":"27.1.15 en"},"content":{"rendered":"\n

The task of detecting overwater objects in poor visibility conditions<\/strong><\/h1>\n\n\n\n

T.C. Nguyen, M.T. Nguyen <\/strong> <\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/p>\n\n\n\n

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Abstract<\/strong>. <\/em>The article is devoted to the problem of detection and recognition of overwater objects from video surveillance data in poor visibility conditions, such as rain, snow, fog, twilight. Along with the problem of visibility degradation there are other factors that complicate the solution of this problem: changes in the shape and size of the image when changing the distance to the object of observation and the angle of view of the video camera. One of the approaches to the problem of video surveillance data processing is discussed \u2013 it consists in the joint application of two technologies: YOLO deep learning model and discrete wavelet image transformation. Experimental results show that the proposed algorithm achieves high accuracy and efficiency, which makes it suitable for application in drone video monitoring systems.<\/p>\n\n\n\n

Keywords<\/strong>: <\/em>object detection problem, YOLO, wavelet transform, overwater objects, drones, poor visibility condition<\/p>\n\n\n\n

For citation<\/strong>.  Nguyen  T.C.,  Nguyen  M.T.  The  task  of  detecting  overwater  objects  in  poor  visibility conditions. News  of  the  Kabardino-Balkarian  Scientific  Center  of  RAS.<\/em>  <\/strong>2025.  Vol.  27.  No.  1.  Pp. 171\u2013180. DOI: 10.35330\/1991-6639-2025-27-1-171-180<\/p>\n\n\n\n

References<\/strong><\/summary>\n
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  1. Wang Z., Wang G., Yang W. Aircraft detection in remote sensing imagery with lightweight feature pyramid network. MIPPR 2019: Automatic Target Recognition and Navigation<\/em>. 2020. Vol. 11429. Pp. 365\u2013369. DOI:10.1117\/12.2539372<\/a><\/li>\n\n\n\n
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  4. De Silva D.D.N., Fernando S., Piyatilake I.T.S., Karunarathne A.V.S. Wavelet based edge feature enhancement for convolutional neural networks<\/a>. Eleventh International Conference on Machine Vision (ICMV 2018).<\/em> 2019. Vol. 11041. DOI: 10.1117\/12.2522849<\/a><\/li>\n\n\n\n
  5. Bahdanau<\/a>  D.,  Cho  K.,  Bengio  Y.  Neural  Machine  Translation  by  Jointly  Learning to Align  and Translate. International <\/em> <\/em>Conference<\/em> <\/em> on <\/em> <\/em>Learning <\/em> <\/em>Representations<\/em>. <\/em> <\/em>ICLR  2015.  DOI: 10.48550\/arXiv.1409.0473<\/li>\n\n\n\n
  6. Niu Z., Zhong G., Yu H. A review on the attention mechanism of deep learning. Neurocomputing, 2021. Vol. 452. Pp. 48\u201362. DOI: 10.1016\/j.neucom.2021.03.091<\/a><\/li>\n\n\n\n
  7. Muhammad Y. What is Yolov8: an in-depth exploration of the Internal features of the next-generation object detector. Computer Vision and Pattern Recognition.<\/em> August 29, 2024.  <\/strong>DOI: <\/strong>10.48550\/arXiv.2408.15857<\/li>\n<\/ol>\n<\/details>\n\n\n\n
    Information about the authors<\/strong><\/summary>\n

    Thanh Cong Nguyen<\/strong>, Post-graduate Student of the Department of System Automatic, MIREA \u2013 Russian Technological University;<\/p>\n\n\n\n

    119454, Russia, Moscow, 78 Vernadsky avenue;<\/p>\n\n\n\n

    congvietnam@mail.ru<\/a>, ORCID: https:\/\/orcid.org\/<\/a>0009-0005-9719-8731<\/p>\n\n\n\n

    Minh Tuong Nguyen<\/strong><\/a>, Candidate of Engineering Sciences, Associate Professor of the Department of Informatics, MIREA \u2013 Russian Technological University;<\/p>\n\n\n\n

    119454, Russia, Moscow, 78 Vernadsky avenue;<\/p>\n\n\n\n

    nguen_m@mirea.ru<\/a>, ORCID: https:\/\/orcid.org\/0009-0002-7267-1121, SPIN-code: 5480-9970<\/p>\n<\/details>\n","protected":false},"excerpt":{"rendered":"

    The task of detecting overwater objects in poor visibility conditions T.C. Nguyen, M.T. Nguyen Abstract. The article is devoted to the problem of detection and recognition of overwater objects from video surveillance data in poor visibility conditions, such as rain, snow, fog, twilight. Along with the problem of visibility degradation there are other factors that […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"wp-custom-template-home","meta":{"footnotes":""},"class_list":["post-1813","page","type-page","status-publish","hentry"],"yoast_head":"\n27.1.15 en - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/27-1-15-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"27.1.15 en - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"The task of detecting overwater objects in poor visibility conditions T.C. Nguyen, M.T. Nguyen Abstract. The article is devoted to the problem of detection and recognition of overwater objects from video surveillance data in poor visibility conditions, such as rain, snow, fog, twilight. 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