Towards All-Time, All-Weather Fod Detection Through Generative AI

In this work, we design a data synthesis pipeline for airport runway Foreign Object Debris (FOD) detection. Our proposed pipeline constructs a synthetic dataset using diffusion models to generate images based on previously acquired images and newly captured image samples, which commonly exhibit different lighting conditions.We explore various training configurations and conduct a comparative analysis of the results. The findings demonstrate that the proposed pipeline effectively boosts the performance of FOD detection, with the model achieving high performance across precision, recall, and mAP50 metrics.