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Object Recognition in Forward Looking Sonar Images using Transfer Learning
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
(Sonar System Design, SAAB Dynamics)
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL. (RPL/EECS)ORCID iD: 0000-0002-7796-1438
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Forward Looking Sonars (FLS) are a typical choiceof sonar for autonomous underwater vehicles. They are mostoften the main sensor for obstacle avoidance and can be usedfor monitoring, homing, following and docking as well. Thosetasks require discrimination between noise and various classes ofobjects in the sonar images. Robust recognition of sonar data stillremains a problem, but if solved it would enable more autonomyfor underwater vehicles providing more reliable informationabout the surroundings to aid decision making. Recent advancesin image recognition using Deep Learning methods have beenrapid. While image recognition with Deep Learning is known torequire large amounts of labeled data, there are data-efficientlearning methods using generic features learned by a networkpre-trained on data from a different domain. This enables usto work with much smaller domain-specific datasets, makingthe method interesting to explore for sonar object recognitionwith limited amounts of training data. We have developed aConvolutional Neural Network (CNN) based classifier for FLS-images and compared its performance to classification usingclassical methods and hand-crafted features.

Place, publisher, year, edition, pages
IEEE, 2018.
Keywords [en]
AUV, CNN, Forward Looking Sonar, Object Recognition, Transfer Learning, Underwater, Data Efficient Learning
National Category
Robotics
Research subject
Vehicle and Maritime Engineering; Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-250893OAI: oai:DiVA.org:kth-250893DiVA, id: diva2:1314046
Conference
2018 IEEE OES Autonomous Underwater Vehicle Symposium
Projects
SMARC SSF IRC15-0046
Funder
Swedish Foundation for Strategic Research , IRC15-0046
Note

QC 20190423

Available from: 2019-05-07 Created: 2019-05-07 Last updated: 2019-05-16Bibliographically approved

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fulltext(876 kB)55 downloads
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CiteExportLink to record
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Citation style
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