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Algorithms for the Detection of First Bottom Returns and Objects in the Water Column in Side-Scan Sonar Images
Universidade de Aveiro, Portugal.
DeepVision AB, Linköping, Sweden.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-5224-8302
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2017 (English)In: OCEANS '17 A Vision for our Marine Future OCEANS '17, Aberdeen, United Kingdom, 2017Conference paper, Published paper (Refereed)
Abstract [en]

Underwater imaging has become an active research area in recent years as an effect of increased interest in underwater environments and is getting potential impact on the world economy, in what is called blue growth. Since sound propagates larger distances than electromagnetic waves underwater, sonar is typically used for underwater imaging. One interesting sonar image setting is comprised of using two parts (left and right) and is usually referred to as sidescan sonar. The image resulted from sidescan sonars, which is called waterfall image, usually has to distinctive parts, the water column and the image seabed. Therefore, the edge separating these two parts, which is called the first bottom return, is the real distance between the sonar and the seabed bottom (which is equivalent to sensor primary altitude). The sensory primary altitude can be measured if the imaging sonar is complemented by interferometric sonar, however, simple sonar systems have no way to measure the first bottom returns other than signal processing techniques. In this work, we propose two methods to detect the first bottom returns; the first is based on smoothing cubic spline regression and the second is based on a moving average filter to detect signal variations. The results of both methods are compared to the sensor primary altitude and have been successful in 22 images out of 25.

Place, publisher, year, edition, pages
Aberdeen, United Kingdom, 2017.
Keyword [en]
Edge detection, cubic smoothing spline, moving average filter, autonomous underwater vehicles
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:mdh:diva-37336DOI: 10.1109/OCEANSE.2017.8084587ISBN: 978-1-5090-5278-3 (electronic)OAI: oai:DiVA.org:mdh-37336DiVA: diva2:1161004
Conference
OCEANS '17 A Vision for our Marine Future OCEANS '17, 19 Jun 2017, Aberdeen, United Kingdom
Projects
Smart and networking underWAter Robots in cooperation MesheSDPAC - Dependable Platforms for Autonomous systems and Control
Available from: 2017-11-28 Created: 2017-11-28 Last updated: 2017-11-28Bibliographically approved

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Frasheri, MirgitaCuruklu, Baran

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Frasheri, MirgitaCuruklu, BaranMartínez, José-Fernán
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