Date Approved


Embargo Period


Document Type


Degree Name

Master of Science


Electrical and Computer Engineering


Henry M. Rowan College of Engineering


Nidhal C. Bouaynaya, Ph.D.

Committee Member 1

Gregory Ditzler, Ph.D.

Committee Member 2

Thomas Kiel


Vision, Drone, Object Detection, Virtual Reality


Machine learning; Military engineering


Electrical and Computer Engineering | Engineering


The introduction of aerial drones on the modern battlefield has transformed combat operations, posing a significant threat to ground-based military operations. Detecting drones in safety scenarios is crucial. However, modern machine learning (ML)-based object detectors struggle to detect small objects like drones. This thesis presents three main contributions: (a) data and algorithmic modifications to improve small object detection in YOLO to aid in drone detection, (b) the development of a benchmark drone detection dataset called DyViR, and (c) the implementation of explainable artificial intelligence (XAI) to ensure transparent and trustworthy decision-making. To boost the performance of small object detection, we introduce the Normalized Wasserstein distance (NWD) into the loss function of our ML model. By incorporating this distance metric, we can effectively handle small object detection by reducing the penalty assigned to small objects, and appropriately balancing the significance of different object sizes. This allows the model to prioritize the accurate detection of small objects, enhancing overall performance. To evaluate our algorithm, we developed and tested the DyViR dataset specifically designed for drone detection research. This synthetic dataset provides a benchmark for assessing drone detection performance. In combat settings, the trustworthiness of ML systems is paramount as their decisions impact user survival. Thus, we implemented explainable AI systems (XAI), specifically Grad-CAM and Eigen-CAM, These techniques provide explanations for the models’ decisions, increasing trust in the system for developers and users.