Date of Award

Spring 5-2021

Degree Type

Masters Thesis

Degree Name

Master of Science (MS)

School

Computing Sciences and Computer Engineering

Committee Chair

Dr Bo Li

Committee Chair School

Computing Sciences and Computer Engineering

Committee Member 2

Dr Lina Pu

Committee Member 2 School

Computing Sciences and Computer Engineering

Committee Member 3

Dr Chaoyang Zhang

Committee Member 3 School

Computing Sciences and Computer Engineering

Abstract

An important feature of an Autonomous Surface Vehicles (ASV) is its capability of automatic object detection to avoid collisions, obstacles and navigate on their own.

Deep learning has made some significant headway in solving fundamental challenges associated with object detection and computer vision. With tremendous demand and advancement in the technologies associated with ASVs, a growing interest in applying deep learning techniques in handling challenges pertaining to autonomous ship driving has substantially increased over the years.

In this thesis, we study, design, and implement an object recognition framework that detects and recognizes objects found in the sea. We first curated a Sea-object Image Dataset (SID) specifically for this project. Then, by utilizing a pre-trained RetinaNet model on a large-scale object detection dataset named Microsoft COCO, we further fine-tune it on our SID dataset. We focused on sea objects that may potentially cause collisions or other types of maritime accidents. Our final model can effectively detect various types of floating or surrounding objects and classify them into one of the ten predefined significant classes, which are buoy, ship, island, pier, person, waves, rocks, buildings, lighthouse, and fish. Experimental results have demonstrated its good performance.

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