Date of Award

3-2021

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering

Department

Department of Electrical and Computer Engineering

First Advisor

Robert C. Leishman, PhD

Abstract

Visual inspection of aircraft skin for surface defects is an area of maintenance that is particularly intensive for time and manpower. One novel way to combat this problem is through the use of computer vision and the advent of Artificial Neural Networks (ANN), or more specifically, semantic segmentation via Convolutional Neural Networks (CNN). The research in the paper explores the use of semantic segmentation of aerial imagery as a way to force feature selection onto key areas of an image that might be more likely to correspond under seasonal variations. Utilizing feature selection and matching on the masked aerial image and the satellite image produces a set of reliable key points that can be used for camera pose estimation and visual navigation.

AFIT Designator

AFIT-ENG-MS-21-M-049

DTIC Accession Number

AD1132718

Comments

Alternate title form: Surface Defect Detection in Aircraft Skin and Visual Navigation based on Forced Feature Selection through Segmentation.

Download

Share

COinS