Architecture Commons^™

Foreign Object Debris Detection For Airport Pavement Images Based On Self-Supervised Localization And Vision Transformer, Travis Munyer, Daniel Brinkman, Xin Zhong, Chenyu Huang, Iason Konstantzos

Durham School of Architectural Engineering and Construction: Faculty Publications

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 openended 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 …