  Supervised object detection methods provide subpar performance when applied
to Foreign Object Debris (FOD) detection because FOD could be arbitrary objects
according to the Federal Aviation Administration (FAA) specification. Current
supervised object detection algorithms require datasets that contain annotated
examples of every to-be-detected object. While a large and expensive dataset
could be developed to include common FOD examples, it is infeasible to collect
all possible FOD examples in the dataset representation because of the
open-ended nature of FOD. Limitations of the dataset could cause FOD detection
systems driven by those supervised algorithms to miss certain FOD, which can
become dangerous to airport operations. To this end, this paper presents a
self-supervised FOD localization by learning to predict the runway images,
which avoids the enumeration of FOD annotation examples. The localization
method utilizes the Vision Transformer (ViT) to improve localization
performance. The experiments show that the method successfully detects
arbitrary FOD in real-world runway situations. The paper also provides an
extension to the localization result to perform classification; a feature that
can be useful to downstream tasks. To train the localization, this paper also
presents a simple and realistic dataset creation framework that only collects
clean runway images. The training and testing data for this method are
collected at a local airport using unmanned aircraft systems (UAS).
Additionally, the developed dataset is provided for public use and further
studies.
