A Review of Foreign Object Debris Detection on Airport Runways: Sensors and Algorithms
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 inspectio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/225 |
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| author | Jingfeng Shan Lapo Miccinesi Alessandra Beni Lorenzo Pagnini Andrea Cioncolini Massimiliano Pieraccini |
| author_facet | Jingfeng Shan Lapo Miccinesi Alessandra Beni Lorenzo Pagnini Andrea Cioncolini Massimiliano Pieraccini |
| author_sort | Jingfeng Shan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-29e68608bf644a99a663928b41fb3c85 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-29e68608bf644a99a663928b41fb3c852025-01-24T13:47:47ZengMDPI AGRemote Sensing2072-42922025-01-0117222510.3390/rs17020225A Review of Foreign Object Debris Detection on Airport Runways: Sensors and AlgorithmsJingfeng Shan0Lapo Miccinesi1Alessandra Beni2Lorenzo Pagnini3Andrea Cioncolini4Massimiliano Pieraccini5Department of Information Engineering, University of Florence, Via Santa Marta 3, 50139 Florence, ItalyDepartment of Information Engineering, University of Florence, Via Santa Marta 3, 50139 Florence, ItalyDepartment of Information Engineering, University of Florence, Via Santa Marta 3, 50139 Florence, ItalyDepartment of Information Engineering, University of Florence, Via Santa Marta 3, 50139 Florence, ItalyDepartment of Information Engineering, University of Florence, Via Santa Marta 3, 50139 Florence, ItalyDepartment of Information Engineering, University of Florence, Via Santa Marta 3, 50139 Florence, ItalyThe 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.https://www.mdpi.com/2072-4292/17/2/225Foreign Object Debris (FOD)synthetic aperture radarartificial intelligencesensor fusionairport runway |
| spellingShingle | Jingfeng Shan Lapo Miccinesi Alessandra Beni Lorenzo Pagnini Andrea Cioncolini Massimiliano Pieraccini A Review of Foreign Object Debris Detection on Airport Runways: Sensors and Algorithms Remote Sensing Foreign Object Debris (FOD) synthetic aperture radar artificial intelligence sensor fusion airport runway |
| title | A Review of Foreign Object Debris Detection on Airport Runways: Sensors and Algorithms |
| title_full | A Review of Foreign Object Debris Detection on Airport Runways: Sensors and Algorithms |
| title_fullStr | A Review of Foreign Object Debris Detection on Airport Runways: Sensors and Algorithms |
| title_full_unstemmed | A Review of Foreign Object Debris Detection on Airport Runways: Sensors and Algorithms |
| title_short | A Review of Foreign Object Debris Detection on Airport Runways: Sensors and Algorithms |
| title_sort | review of foreign object debris detection on airport runways sensors and algorithms |
| topic | Foreign Object Debris (FOD) synthetic aperture radar artificial intelligence sensor fusion airport runway |
| url | https://www.mdpi.com/2072-4292/17/2/225 |
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