Symbology Detection and Numerical Recognition for T-38 Heads-Up Display Recordings

Author

Ben T. Hepner

Date of Award

3-2025

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Operational Sciences

First Advisor

Nathan B. Gaw, PhD

Abstract

The extraction of symbology and numerical data from the T-38 Heads-Up Display (HUD) enhances post-flight analysis and supports real-time decision-making. This research develops a deep learning pipeline using YOLO-based object detection and Optical Character Recognition (OCR) to analyze HUD video data. Model evaluations showed mAP0.5:0.95 ranging from 0.422 (YOLOv11m, hard test set) to 0.696 (YOLOv8m, medium test set), demonstrating robust symbology detection. Numeric detection performed well (mAP_0.5:0.95 = 0.764), but OCR struggled with glare and resolution limitations, achieving a recognition accuracy of 17.35%. These results validate deep learning for HUD data extraction but highlight the need for improved robustness in degraded conditions and real-time implementation.

AFIT Designator

AFIT-ENS-MS-25-M-168

Comments

An embargo was observed for this posting.

Distribution A: Approved for public release, Distribution Unlimited. PA case number 88abw-2025-0414

Recommended Citation

Hepner, Ben T., "Symbology Detection and Numerical Recognition for T-38 Heads-Up Display Recordings" (2025). Theses and Dissertations. 8279.
https://scholar.afit.edu/etd/8279