Author Type

Graduate Student

Date of Award

2018

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Dhanak, Manhar R.

Abstract

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.

Extent

109 p.

Comments

Includes bibliography.|Thesis (M.S.)--Florida Atlantic University, 2018.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00013025

Format

pdf

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