A Review of Foreign Object Debris Detection on Airport Runways: Sensors and Algorithms

Abstract

The detection of Foreign Object Debris (FOD) is crucial for maintaining safety in critical areas like airport runways. This paper presents a comprehensive review of FOD detection technologies, covering traditional, radar-based, and artificial intelligence (AI)-driven methods. Manual visual inspection and optical sensors, while widely used, are limited in scalability and reliability under adverse conditions. Radar technologies, such as millimeter-wave radar and synthetic aperture radar, offer robust performance, with advancements in algorithms and sensor fusion significantly enhancing their effectiveness. AI approaches, employing supervised and unsupervised learning, demonstrate potential for automating detection and improving precision, although challenges such as limited datasets and high computational demands persist. This review consolidates the recent progress across these domains, highlighting the need for integrated systems that combine radar and AI to improve adaptability, scalability, and small-FOD detection. By addressing these limitations, the study provides insights into future research directions and the development of innovative FOD detection solutions, contributing to safer and more efficient operational environments.