Automatic target recognition capabilities in autonomous underwater vehicles has
been a daunting task, largely due to the noisy nature of sonar imagery and due to the lack
of publicly available sonar data. Machine learning techniques have made great strides in
tackling this feat, although not much research has been done regarding deep learning
techniques for side-scan sonar imagery. Here, a state-of-the-art deep learning object
detection method is adapted for side-scan sonar imagery, with results supporting a simple
yet robust method to detect objects/anomalies along the seabed. A systematic procedure
was employed in transfer learning a pre-trained convolutional neural network in order to
learn the pixel-intensity based features of seafloor anomalies in sonar images. Using this
process, newly trained convolutional neural network models were produced using
relatively small training datasets and tested to show reasonably accurate anomaly
detection and classification with little to no false alarms. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection
| Identifer | oai:union.ndltd.org:fau.edu/oai:fau.digital.flvc.org:fau_40799 |
| Contributors | Einsidler, Dylan (author), Dhanak, Manhar R. (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering |
| Publisher | Florida Atlantic University |
| Source Sets | Florida Atlantic University |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation, Text |
| Format | 109 p., application/pdf |
| Rights | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder., http://rightsstatements.org/vocab/InC/1.0/ |
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