Publications

2026

1. 

   DualFOD: A Dual-Modality Deep Learning Framework for UAS-Based Foreign Object Debris Detection Using Thermal and RGB Imagery

   Owais Ahmed, Caleb S. Caldwell, and Adeel Khalid

   Drones, Mar 2026

   Abs HTML PDF

   Foreign Object Debris (FOD) poses critical risks to aircraft during takeoff and landing, resulting in billions of dollars in losses annually due to infrastructure damage and flight delays. This study proposes DualFOD, a dual-modality detection framework that integrates a supervised YOLO12-based RGB detector with an unsupervised thermal anomaly extraction pipeline for identifying debris on runway surfaces. A decision-level fusion algorithm combines detections from both branches using spatial proximity matching to produce a unified FOD inventory. The RGB branch achieves a precision of 0.954 and mAP@0.5 of 0.890 on the held-out test set. Cross-site validation at the Cobb County Sport Aviation Complex demonstrates that thermal detection recovers debris missed by RGB at higher altitudes, with the fused output consistently outperforming either single-modality branch.

2024

1. 

   An Integrated Approach to Robotic Object Grasping and Manipulation

   Owais Ahmed, M Huzaifa, M Areeb , and 1 more author

   Nov 2024

   Abs HTML PDF

   Developing a system that can determine the precise position and orientation (6D pose) of objects and perform autonomous grasping not only enhances warehouse operations but also advances the field of robotics. This project represents an integration of advanced technologies, including computer vision, machine learning, and robotics. By using two monocular cameras to determine the 6D poses of objects and training a machine learning model to predict optimal grasping strategies, the goal is to create a flexible system capable of handling a wide range of objects.