A Complete Process For Shipborne Sea-Ice Field Analysis Using Machine Vision
A sensor instrumentation and an automated process are proposed for sea-ice field analysis using ship mounted machine vision cameras with the help of inertial and satellite positioning sensors. The proposed process enables automated acquisition of sea-ice concentration, floes size and distribution. T...
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ftaaltouniv:oai:aaltodoc.aalto.fi:123456789/107569 2024-04-28T08:37:34+00:00 A Complete Process For Shipborne Sea-Ice Field Analysis Using Machine Vision Sandru, Andrei Hyyti, Heikki Visala, Arto Kujala, Pentti Department of Electrical Engineering and Automation Department of Mechanical Engineering Autonomous Systems Marine Technology Aalto-yliopisto Aalto University 2020-11 7 14539-14545 application/pdf https://aaltodoc.aalto.fi/handle/123456789/107569 https://doi.org/10.1016/j.ifacol.2020.12.1458 en eng Elsevier Science Publishers BV IFAC-PapersOnLine Volume 53, issue 2 Sandru, A, Hyyti, H, Visala, A & Kujala, P 2020, ' A Complete Process For Shipborne Sea-Ice Field Analysis Using Machine Vision ', IFAC-PapersOnLine, vol. 53, no. 2, pp. 14539-14545 . https://doi.org/10.1016/j.ifacol.2020.12.1458 2405-8963 PURE UUID: dc308262-7627-4341-9571-935cbd46e558 PURE ITEMURL: https://research.aalto.fi/en/publications/dc308262-7627-4341-9571-935cbd46e558 PURE LINK: http://www.scopus.com/inward/record.url?scp=85105051483&partnerID=8YFLogxK PURE FILEURL: https://research.aalto.fi/files/62871934/ELEC_Sandru_etal_A_Complete_Process_for_Shipborne_IFAC_finalpublishedversion.pdf https://aaltodoc.aalto.fi/handle/123456789/107569 URN:NBN:fi:aalto-202105196833 doi:10.1016/j.ifacol.2020.12.1458 openAccess machine vision Sea-ice k-means dynamic thresholding IMU sensor integration Decision Support System (DSS) Conference article publishedVersion 2020 ftaaltouniv https://doi.org/10.1016/j.ifacol.2020.12.1458 2024-04-10T00:21:31Z A sensor instrumentation and an automated process are proposed for sea-ice field analysis using ship mounted machine vision cameras with the help of inertial and satellite positioning sensors. The proposed process enables automated acquisition of sea-ice concentration, floes size and distribution. The process contains pre-processing steps such as sensor calibration, distortion removal, orthorectification of image data, and data extraction steps such as sea-ice floe clustering, detection, and analysis. In addition, we improve the state of the art of floe clustering and detection, by using an enhanced version of the k-means algorithm and the blue colour channel for increased contrast in ice detection. Comparing to manual visual observations, the proposed method gives significantly more detailed and frequent data about the size and distribution of individual floes. Through our initial experiments in pack ice conditions,the proposed system has proved to be able to segment most of the individual floes and estimate their size and area. Peer reviewed Conference Object Sea ice Aalto University Publication Archive (Aaltodoc) IFAC-PapersOnLine 53 2 14539 14545 |
institution |
Open Polar |
collection |
Aalto University Publication Archive (Aaltodoc) |
op_collection_id |
ftaaltouniv |
language |
English |
topic |
machine vision Sea-ice k-means dynamic thresholding IMU sensor integration Decision Support System (DSS) |
spellingShingle |
machine vision Sea-ice k-means dynamic thresholding IMU sensor integration Decision Support System (DSS) Sandru, Andrei Hyyti, Heikki Visala, Arto Kujala, Pentti A Complete Process For Shipborne Sea-Ice Field Analysis Using Machine Vision |
topic_facet |
machine vision Sea-ice k-means dynamic thresholding IMU sensor integration Decision Support System (DSS) |
description |
A sensor instrumentation and an automated process are proposed for sea-ice field analysis using ship mounted machine vision cameras with the help of inertial and satellite positioning sensors. The proposed process enables automated acquisition of sea-ice concentration, floes size and distribution. The process contains pre-processing steps such as sensor calibration, distortion removal, orthorectification of image data, and data extraction steps such as sea-ice floe clustering, detection, and analysis. In addition, we improve the state of the art of floe clustering and detection, by using an enhanced version of the k-means algorithm and the blue colour channel for increased contrast in ice detection. Comparing to manual visual observations, the proposed method gives significantly more detailed and frequent data about the size and distribution of individual floes. Through our initial experiments in pack ice conditions,the proposed system has proved to be able to segment most of the individual floes and estimate their size and area. Peer reviewed |
author2 |
Department of Electrical Engineering and Automation Department of Mechanical Engineering Autonomous Systems Marine Technology Aalto-yliopisto Aalto University |
format |
Conference Object |
author |
Sandru, Andrei Hyyti, Heikki Visala, Arto Kujala, Pentti |
author_facet |
Sandru, Andrei Hyyti, Heikki Visala, Arto Kujala, Pentti |
author_sort |
Sandru, Andrei |
title |
A Complete Process For Shipborne Sea-Ice Field Analysis Using Machine Vision |
title_short |
A Complete Process For Shipborne Sea-Ice Field Analysis Using Machine Vision |
title_full |
A Complete Process For Shipborne Sea-Ice Field Analysis Using Machine Vision |
title_fullStr |
A Complete Process For Shipborne Sea-Ice Field Analysis Using Machine Vision |
title_full_unstemmed |
A Complete Process For Shipborne Sea-Ice Field Analysis Using Machine Vision |
title_sort |
complete process for shipborne sea-ice field analysis using machine vision |
publisher |
Elsevier Science Publishers BV |
publishDate |
2020 |
url |
https://aaltodoc.aalto.fi/handle/123456789/107569 https://doi.org/10.1016/j.ifacol.2020.12.1458 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
IFAC-PapersOnLine Volume 53, issue 2 Sandru, A, Hyyti, H, Visala, A & Kujala, P 2020, ' A Complete Process For Shipborne Sea-Ice Field Analysis Using Machine Vision ', IFAC-PapersOnLine, vol. 53, no. 2, pp. 14539-14545 . https://doi.org/10.1016/j.ifacol.2020.12.1458 2405-8963 PURE UUID: dc308262-7627-4341-9571-935cbd46e558 PURE ITEMURL: https://research.aalto.fi/en/publications/dc308262-7627-4341-9571-935cbd46e558 PURE LINK: http://www.scopus.com/inward/record.url?scp=85105051483&partnerID=8YFLogxK PURE FILEURL: https://research.aalto.fi/files/62871934/ELEC_Sandru_etal_A_Complete_Process_for_Shipborne_IFAC_finalpublishedversion.pdf https://aaltodoc.aalto.fi/handle/123456789/107569 URN:NBN:fi:aalto-202105196833 doi:10.1016/j.ifacol.2020.12.1458 |
op_rights |
openAccess |
op_doi |
https://doi.org/10.1016/j.ifacol.2020.12.1458 |
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53 |
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14539 |
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