Analysis of the Possibility to Detect Fake Vessels in the Automatic Identification System

Jakovlev, Sergej; Daranda, Andrius; Vozňák, Miroslav; Lektauers, Arnis; Eglynas, Tomas; Jusis, Mindaugas

doi:10.1109/itms51158.2020.9259293

Cited by 4 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This leaves room for real-time systems to analyse the situation several times per second and therefore help to prevent or respond to complications. Moreover, since it is an open standard, AIS presents some vulnerabilities to cyber threats, which include spoofing, highjacking attacks or denial of service [ 5 , 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Ship Segmentation and Georeferencing from Static Oblique View Images

Carrillo-Perez¹,

Barnes²,

Stephan³

2022

Sensors

View full text Add to dashboard Cite

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.

show abstract