Ship Segmentation and Georeferencing from Static Oblique View Images
Camera systems support the rapid assessment of ship traffic at ports, allowing for a better perspective of the maritime situation. However, optimal ship monitoring requires a level of automation that allows personnel to keep track of relevant variables in the maritime situation in an understandable...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/7/2713 |
| _version_ | 1797437645461127168 |
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| author | Borja Carrillo-Perez Sarah Barnes Maurice Stephan |
| author_facet | Borja Carrillo-Perez Sarah Barnes Maurice Stephan |
| author_sort | Borja Carrillo-Perez |
| collection | DOAJ |
| description | Camera systems support the rapid assessment of ship traffic at ports, allowing for a better perspective of the maritime situation. However, optimal ship monitoring requires a level of automation that allows personnel to keep track of relevant variables in the maritime situation in an understandable and visualisable format. It therefore becomes important to have real-time recognition of ships present at the infrastructure, with their class and geographic position presented to the maritime situational awareness operator. This work presents a novel dataset, ShipSG, for the segmentation and georeferencing of ships in maritime monitoring scenes with a static oblique view. Moreover, an exploration of four instance segmentation methods, with a focus on robust (Mask-RCNN, DetectoRS) and real-time performances (YOLACT, Centermask-Lite) and their generalisation to other existing maritime datasets, is shown. Lastly, a method for georeferencing ship masks is proposed. This includes an automatic calculation of the pixel of the segmented ship to be georeferenced and the use of a homography to transform this pixel to geographic coordinates. DetectoRS provided the highest ship segmentation mAP of 0.747. The fastest segmentation method was Centermask-Lite, with 40.96 FPS. The accuracy of our georeferencing method was (22 ± 10) m for ships detected within a 400 m range, and (53 ± 24) m for ships over 400 m away from the camera. |
| first_indexed | 2024-03-09T11:25:20Z |
| format | Article |
| id | doaj.art-7068b67f49b2403fae699ee48cbe324f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:25:20Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7068b67f49b2403fae699ee48cbe324f2023-12-01T00:04:04ZengMDPI AGSensors1424-82202022-04-01227271310.3390/s22072713Ship Segmentation and Georeferencing from Static Oblique View ImagesBorja Carrillo-Perez0Sarah Barnes1Maurice Stephan2German Aerospace Center (DLR), Institute for the Protection of Maritime Infrastructures, Fischkai 1, 27572 Bremerhaven, GermanyGerman Aerospace Center (DLR), Institute for the Protection of Maritime Infrastructures, Fischkai 1, 27572 Bremerhaven, GermanyGerman Aerospace Center (DLR), Institute for the Protection of Maritime Infrastructures, Fischkai 1, 27572 Bremerhaven, GermanyCamera systems support the rapid assessment of ship traffic at ports, allowing for a better perspective of the maritime situation. However, optimal ship monitoring requires a level of automation that allows personnel to keep track of relevant variables in the maritime situation in an understandable and visualisable format. It therefore becomes important to have real-time recognition of ships present at the infrastructure, with their class and geographic position presented to the maritime situational awareness operator. This work presents a novel dataset, ShipSG, for the segmentation and georeferencing of ships in maritime monitoring scenes with a static oblique view. Moreover, an exploration of four instance segmentation methods, with a focus on robust (Mask-RCNN, DetectoRS) and real-time performances (YOLACT, Centermask-Lite) and their generalisation to other existing maritime datasets, is shown. Lastly, a method for georeferencing ship masks is proposed. This includes an automatic calculation of the pixel of the segmented ship to be georeferenced and the use of a homography to transform this pixel to geographic coordinates. DetectoRS provided the highest ship segmentation mAP of 0.747. The fastest segmentation method was Centermask-Lite, with 40.96 FPS. The accuracy of our georeferencing method was (22 ± 10) m for ships detected within a 400 m range, and (53 ± 24) m for ships over 400 m away from the camera.https://www.mdpi.com/1424-8220/22/7/2713ship datasetinstance segmentationship georeferencinghomography |
| spellingShingle | Borja Carrillo-Perez Sarah Barnes Maurice Stephan Ship Segmentation and Georeferencing from Static Oblique View Images Sensors ship dataset instance segmentation ship georeferencing homography |
| title | Ship Segmentation and Georeferencing from Static Oblique View Images |
| title_full | Ship Segmentation and Georeferencing from Static Oblique View Images |
| title_fullStr | Ship Segmentation and Georeferencing from Static Oblique View Images |
| title_full_unstemmed | Ship Segmentation and Georeferencing from Static Oblique View Images |
| title_short | Ship Segmentation and Georeferencing from Static Oblique View Images |
| title_sort | ship segmentation and georeferencing from static oblique view images |
| topic | ship dataset instance segmentation ship georeferencing homography |
| url | https://www.mdpi.com/1424-8220/22/7/2713 |
| work_keys_str_mv | AT borjacarrilloperez shipsegmentationandgeoreferencingfromstaticobliqueviewimages AT sarahbarnes shipsegmentationandgeoreferencingfromstaticobliqueviewimages AT mauricestephan shipsegmentationandgeoreferencingfromstaticobliqueviewimages |