Date of Award
6-2024
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Operational Sciences
First Advisor
Bruce A. Cox, PhD
Abstract
Data exploitation techniques are the enabler for technological advancements in military ISR applications of ladar ISR. By identifying instances of military objects in observed scenes, point cloud deep learning models can unlock new standards of real-time information delivery to warfighters. Although current deep learning training datasets do not include real-world collection occlusions consistent with military applications, this research characterizes SPT model performance by adding occlusions to the DALESObjects dataset via artificial flyby simulations.We find that a baseline model trained on unoccluded data suffers performance degradation on both semantic and instance segmentation tasks when evaluated on occluded data, but that the effects can be minimized by collecting maximum angular coverage of the scene, especially at balanced observation elevations that stay away from extremely shallow or steep angles of collection. Beyond the most occluded edge cases, the model’s ability to identify vehicle instances within a scene was largely disconnected from the percentage of vehicle points observed. This suggests that identifying objects of interest within a scene can be done with a relatively sparse representation of that scene. Specialized models trained on occluded data exhibit increased performance on representative data compared to the baseline model. Matching training data to testing data maximizes performance, with degradations as testing data diverges in elevation or decreases in angular coverage. Increasing angular coverage can result in increased model performance but not above the performance of a network specially trained on that level of angular coverage.
AFIT Designator
AFIT-ENS-MS-24-J-030
Recommended Citation
Gibson, Andrew D., "Impact of Operational Ladar Occlusions on Point Cloud Instance Segmentation" (2024). Theses and Dissertations. 7810.
https://scholar.afit.edu/etd/7810
Comments
A 12-month embargo was observed for posting this work on AFIT Scholar.
Distribution Statement A, Approved for Public Release. PA case number on file.