elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Ship segmentation and georeferencing from static oblique view images

Carrillo-Perez, Borja and Barnes, Sarah and Stephan, Maurice (2022) Ship segmentation and georeferencing from static oblique view images. Sensors, 22 (7). Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/s22072713. ISSN 1424-8220.

[img] PDF - Published version
8MB

Official URL: https://www.mdpi.com/1424-8220/22/7/2713

Abstract

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.

Item URL in elib:https://elib.dlr.de/186015/
Document Type:Article
Title:Ship segmentation and georeferencing from static oblique view images
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Carrillo-Perez, BorjaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Barnes, SarahUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Stephan, MauriceUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:1 April 2022
Journal or Publication Title:Sensors
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:22
DOI:10.3390/s22072713
Editors:
EditorsEmailEditor's ORCID iDORCID Put Code
Carrillo-Perez, BorjaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Barnes, SarahUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Stephan, MauriceUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Series Name:Sensing and Imaging
ISSN:1424-8220
Status:Published
Keywords:ship dataset; instance segmentation; ship georeferencing; homography
HGF - Research field:other
HGF - Program:other
HGF - Program Themes:other
DLR - Research area:no assignment
DLR - Program:no assignment
DLR - Research theme (Project):no assignment
Location: Bremerhaven
Institutes and Institutions:Institute for the Protection of Maritime Infrastructures > Maritime Security Technologies
Deposited By: Carrillo Perez, Borja Jesus
Deposited On:11 Apr 2022 07:28
Last Modified:14 Apr 2022 12:18

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Website and database design: Copyright © German Aerospace Center (DLR). All rights reserved.