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Characterization of A Stereo Vision System For Object Classification For USV Navigation

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Date Issued:
2022
Abstract/Description:
This experiment used different methodologies and comparisons that helped to determine the direction of future research on water-based perception systems for unmanned surface vehicles (USV) platforms. This would be using a stereo-vison based system. Presented in this work is object color and shape classification in the real-time maritime environment. This was coupled with HSV color space that allowed for different thresholds to be identified and detected. The algorithm was then calibrated and executed to configure the depth, color and shape accuracies. The approach entails the characterization of a stereo-vision camera and mount that was designed with 8.5° horizontal viewing increments and mounted on the WAMV. This characterization has depth, color and shape object detection and its classification. Different shapes and buoys were used to complete the testing with assorted colors and shapes. The main program used was OpenCV which entails Gaussian blurring, Morphological operators and Canny edge detection libraries with a ROS integration. The code focuses on the area size and the number of contours detected on the shape for successes. A summary of what this thesis entails is the installation and characterization of the stereovision system on the WAMV-USV by obtaining specific inputs to the high-level controller.
Title: Characterization of A Stereo Vision System For Object Classification For USV Navigation.
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Name(s): Kaplowitz, Chad , author
Dhanak, Manhar, Thesis advisor
Florida Atlantic University, Degree grantor
Department of Ocean and Mechanical Engineering
College of Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2022
Date Issued: 2022
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 118 P.
Language(s): English
Abstract/Description: This experiment used different methodologies and comparisons that helped to determine the direction of future research on water-based perception systems for unmanned surface vehicles (USV) platforms. This would be using a stereo-vison based system. Presented in this work is object color and shape classification in the real-time maritime environment. This was coupled with HSV color space that allowed for different thresholds to be identified and detected. The algorithm was then calibrated and executed to configure the depth, color and shape accuracies. The approach entails the characterization of a stereo-vision camera and mount that was designed with 8.5° horizontal viewing increments and mounted on the WAMV. This characterization has depth, color and shape object detection and its classification. Different shapes and buoys were used to complete the testing with assorted colors and shapes. The main program used was OpenCV which entails Gaussian blurring, Morphological operators and Canny edge detection libraries with a ROS integration. The code focuses on the area size and the number of contours detected on the shape for successes. A summary of what this thesis entails is the installation and characterization of the stereovision system on the WAMV-USV by obtaining specific inputs to the high-level controller.
Identifier: FA00014035 (IID)
Degree granted: Thesis (MS)--Florida Atlantic University, 2022.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Computer vision
Unmanned surface vehicles
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00014035
Use and Reproduction: 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.
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.