  Foreign Object Debris (FOD) detection has attracted increased attention in
the area of machine learning and computer vision. However, a robust and
publicly available image dataset for FOD has not been initialized. To this end,
this paper introduces an image dataset of FOD, named FOD in Airports (FOD-A).
FOD-A object categories have been selected based on guidance from prior
documentation and related research by the Federal Aviation Administration
(FAA). In addition to the primary annotations of bounding boxes for object
detection, FOD-A provides labeled environmental conditions. As such, each
annotation instance is further categorized into three light level categories
(bright, dim, and dark) and two weather categories (dry and wet). Currently,
FOD-A has released 31 object categories and over 30,000 annotation instances.
This paper presents the creation methodology, discusses the publicly available
dataset extension process, and demonstrates the practicality of FOD-A with
widely used machine learning models for object detection.
