Work

Automatic Detection and Predictive Geo-location of Foreign Object Debris on Airport Runway

Year: 2025

Type: article

Abstract: —This paper presents an advanced Foreign Object Debris (FOD) detection system for airport runways using YOLOv8 and a geolocation prediction model based on machine learning regression. By integrating a self- attention mechanism with CNNs, the system achieves high detection accuracy, especially for small objects under challenging weather conditions. Ablation studies show improved Mean Average Precision (mAP), and comparisons with YOLOv5, YOLOX, and YOLOv7 confirm YOLOv8's superior performance. The geolocation model further enhances practical deployment for real- world FOD detection and removal.

Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT

DOI: 10.55041/ijsrem.ncft004

Authors: Kothagundla Sahithi, Kalwakolu Ganesh, ,Mahankali Uday Kiran, Padma Rajani

Institution: Guru Nanak Institutions

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: bronze

OpenAlex ID: W4411277830

FWCI:

Cites: 13

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Related to: 10