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En studie över tillämpligheten av deep learning inom sjöövervakning
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Exploring the capabilities of deep learning in seasurveillance (English)
Abstract [en]

In this master thesis deep learning is proven to be applicable in the field of seasurveillance. Commercial ships using the AIS system have to report the type of thevessel such as fishing ship or cargo ship. A problem with AIS data is that it can beeasily manipulated and therefore deliberately or accidentally incorrect. This thesis will focus on detecting false ship types. To detect a false ship type 19 different methods were tested on the 1100 hour long AIS data set. Three of these methods were baseline methods using a more conventional approach to the sea surveillanceproblem. The testing showed that the best performing method was one of the deeplearning methods proving that deep learning is indeed suitable in sea surveillance.

Abstract [sv]

Detta examensarbetet bevisar att neurala nätverk är tillämpningsbara inom sjöövervakning. Kommersiella skepp som använder Automatic Identification System(AIS) måste rapportera sin skeppstyp exempelvis fiskebåt eller transportbåt. Ett problem med AIS data är att den är lätt att manipulera och kan därför medvetet eller omedvetet vara felaktig. Detta examensarbete fokuserar på att upptäcka falska skeppstyper genom att analysera båtrörelser. För att upptäcka falska skeppstyper har 19 olika metoder testats på en 1100 timmar lång AIS datamängd. Tre av dessa metoder var standardmetoder som var uppbyggda genom mer konventionella tillvägagångssätt.Testerna visade att den bäst lämpade metoden för sjöövervakning varen av deep learning metoderna.

Place, publisher, year, edition, pages
2017.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-217526OAI: oai:DiVA.org:kth-217526DiVA: diva2:1156734
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Available from: 2017-11-14 Created: 2017-11-14 Last updated: 2017-11-14Bibliographically approved

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