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

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. Advancements in automated inspection technologies have enabled the use of Unmanned Aerial Systems (UAS) combined with Artificial Intelligence (AI) for rapid FOD identification. While prior research has extensively evaluated optical sensors such as RGB imaging and radar, limited work has investigated the potential of thermal imaging for improved FOD visibility under challenging environmental conditions. 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. This research contributes toward scalable autonomous FOD monitoring that enhances operational safety in aviation environments.