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Object Recognition in Forward Looking Sonar Images using Transfer Learning
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
Sonar System Design Saab Dynamics, Linköping, Sweden.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-7796-1438
2018 (English)In: AUV 2018 - 2018 IEEE/OES Autonomous Underwater Vehicle Workshop, Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2018Conference paper, Published paper (Refereed)
Abstract [en]

Forward Looking Sonars (FLS) are a typical choice of sonar for autonomous underwater vehicles. They are most often the main sensor for obstacle avoidance and can be used for monitoring, homing, following and docking as well. Those tasks require discrimination between noise and various classes of objects in the sonar images. Robust recognition of sonar data still remains a problem, but if solved it would enable more autonomy for underwater vehicles providing more reliable information about the surroundings to aid decision making. Recent advances in image recognition using Deep Learning methods have been rapid. While image recognition with Deep Learning is known to require large amounts of labeled data, there are data-efficient learning methods using generic features learned by a network pre-trained on data from a different domain. This enables us to work with much smaller domain-specific datasets, making the method interesting to explore for sonar object recognition with limited amounts of training data. We have developed a Convolutional Neural Network (CNN) based classifier for FLS-images and compared its performance to classification using classical methods and hand-crafted features.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018.
Keywords [en]
AUV, CNN, Data Efficient Learning, Forward Looking Sonar, Object Recognition, Transfer Learning, Underwater, Autonomous vehicles, Decision making, Deep learning, Image recognition, Neural networks, Sonar, Underwater acoustics, Convolutional neural network, Different domains, Efficient learning, Forward looking sonars, Robust recognition, Underwater vehicles, Autonomous underwater vehicles
National Category
Robotics Vehicle Engineering
Identifiers
URN: urn:nbn:se:kth:diva-262418DOI: 10.1109/AUV.2018.8729686ISI: 000492901600001Scopus ID: 2-s2.0-85068350418ISBN: 9781728102535 (print)OAI: oai:DiVA.org:kth-262418DiVA, id: diva2:1365409
Conference
2018 IEEE/OES Autonomous Underwater Vehicle Workshop, AUV 2018, 6-9 November 2018, Porto, Portugal
Note

QC 20191024

Available from: 2019-10-24 Created: 2019-10-24 Last updated: 2019-12-09Bibliographically approved

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Rixon Fuchs, LouiseFolkesson, John

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