國立虎尾科技大學 |

Autonomous Shipwreck Detection & Mapping.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Autonomous Shipwreck Detection & Mapping./
Author:	Ard, William.
Description:	1 online resource (63 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-06.
Contained By:	Masters Abstracts International85-06.
Subject:	Autonomous underwater vehicles. -
Online resource:	click for full text (PQDT)
ISBN:	9798381015430

Autonomous Shipwreck Detection & Mapping.
Ard, William.

Autonomous Shipwreck Detection & Mapping. - 1 online resource (63 pages)

Source: Masters Abstracts International, Volume: 85-06.

Thesis (M.Sc.)--Louisiana State University and Agricultural & Mechanical College, 2023.

Includes bibliographical references

This thesis presents the development and testing of Bruce, a low-cost hybrid Remotely Operated Vehicle (ROV)/Autonomous Underwater Vehicle (AUV) system for the optical survey of marine archaeological sites, as well as a novel sonar image augmentation strategy for semantic segmentation of shipwrecks. This approach takes side-scan sonar and bathymetry data collected using an EdgeTech 2205 AUV sensor integrated with an Harris Iver3, and generates augmented image data to be used for the semantic segmentation of shipwrecks. It is shown that, due to the feature enhancement capabilities of the proposed shipwreck detection strategy, correctly identified areas have a 15% higher mean accuracy than using sonar imagery alone. Furthermore, mean intersection over union shows an increase of 9.5% using the augmented images.The hybrid ROV/AUV is then used to autonomously collect optical images of the identified shipwrecks. Stereo images are collected using custom camera systems that incorporate the Zed 2i and Zed Mini AI machine vision cameras. The system uses coverage path plans for the surveys generated using two different planners. The performance of the system is validated using tests in a controlled environment and at real-world shipwreck sites. The results from the recorded vehicle odometry show that the system can autonomously track the paths provided to it.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381015430Subjects--Topical Terms:

1468047
Autonomous underwater vehicles.
Index Terms--Genre/Form:

554714
Electronic books.

Autonomous Shipwreck Detection & Mapping.
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500 $a Source: Masters Abstracts International, Volume: 85-06.
500 $a Advisor: Barbalata, Corina.
502 $a Thesis (M.Sc.)--Louisiana State University and Agricultural & Mechanical College, 2023.
504 $a Includes bibliographical references
520 $a This thesis presents the development and testing of Bruce, a low-cost hybrid Remotely Operated Vehicle (ROV)/Autonomous Underwater Vehicle (AUV) system for the optical survey of marine archaeological sites, as well as a novel sonar image augmentation strategy for semantic segmentation of shipwrecks. This approach takes side-scan sonar and bathymetry data collected using an EdgeTech 2205 AUV sensor integrated with an Harris Iver3, and generates augmented image data to be used for the semantic segmentation of shipwrecks. It is shown that, due to the feature enhancement capabilities of the proposed shipwreck detection strategy, correctly identified areas have a 15% higher mean accuracy than using sonar imagery alone. Furthermore, mean intersection over union shows an increase of 9.5% using the augmented images.The hybrid ROV/AUV is then used to autonomously collect optical images of the identified shipwrecks. Stereo images are collected using custom camera systems that incorporate the Zed 2i and Zed Mini AI machine vision cameras. The system uses coverage path plans for the surveys generated using two different planners. The performance of the system is validated using tests in a controlled environment and at real-world shipwreck sites. The results from the recorded vehicle odometry show that the system can autonomously track the paths provided to it.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Autonomous underwater vehicles. $3 1468047
650 4 $a Sensors. $3 1003702
650 4 $a Neural networks. $3 1011215
650 4 $a Shipwrecks. $3 964991
650 4 $a Robotics. $3 561941
650 4 $a Transportation. $3 558117
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773 0 $t Masters Abstracts International $g 85-06.
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