Framework for UAS-Integrated Airport Runway Design Code Compliance Using Incremental Mosaic Imagery

Abstract

Airport inspections are essential to ensuring the safety of aircraft operation for the general public. The current, manual, approach to inspection consumes a significant amount of time. Unmanned aircraft systems (UASs) are often employed to improve the efficiency of visual inspections of various infrastructure facilities. However, only a few studies have documented their use in the context of airport inspections. This study proposes a framework for inspecting runway design codes (RDCs) for airfields that relies on mosaic imagery. Scale-invariant feature transform and best bin first algorithms were integrated to generate accurate UAS-based mosaic imagery for airports. A fixed-wing UAS platform was deployed to capture aerial images of an airport testbed provided by the DOT. The validation results showed that the framework had a high enough level of accuracy to measure pixel-based distances for runway design code (RDC) items that were comparable to the results of manual airport inspections. Across all RDC items in this study, an average error rate of 4.664% between the two types of inspection was documented. The lowest level of error (1.520%) was recorded for the distance between the runway centerline and aircraft parking area, and the highest (11.200%) corresponded to taxiway width. This study contributes to a better understanding of UAS-based airport inspection applications and strengthens and broadens the utility of UASs in visual inspections of civil infrastructure systems.