Human-in-the-loop for autonomous underwater threat recognition

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdragepeer review

Samenvatting

In this paper, human expert operators and automated classification algorithms are charged with the task of analyzing sonar images collected during real mine countermeasures exercises in order to detect and classify targets. Images are collected using synthetic aperture sonar (SAS) and side scan sonar (SSS), covering a test area on the Belgian Continental Shelf, between the Thorton bank and the Goote Bank. A seafloor segmentation map of this area, calculated using lacunarity and representing how difficult or how benign the seafloor is for object-recognition, is used as a new strategy in order to divide the database between operator and computer. Results demonstrate the utility of considering the human operator as an integral part of the automatic underwater object recognition process, and demonstrate how automated algorithms can extend and complement human performances.

Originele taal-2Engels
TitelOCEANS 2018 MTS/IEEE Charleston, OCEAN 2018
UitgeverijInstitute of Electrical and Electronics Engineers Inc.
ISBN van elektronische versie9781538648148
DOI's
StatusGepubliceerd - 7 jan. 2019
EvenementOCEANS 2018 MTS/IEEE Charleston, OCEANS 2018 - Charleston, Verenigde Staten van Amerika
Duur: 22 okt. 201825 okt. 2018

Publicatie series

NaamOCEANS 2018 MTS/IEEE Charleston, OCEAN 2018

Congres

CongresOCEANS 2018 MTS/IEEE Charleston, OCEANS 2018
Land/RegioVerenigde Staten van Amerika
StadCharleston
Periode22/10/1825/10/18

Vingerafdruk

Duik in de onderzoeksthema's van 'Human-in-the-loop for autonomous underwater threat recognition'. Samen vormen ze een unieke vingerafdruk.

Citeer dit