Application of Deep Learning to the Detection of Foreign Object Debris at Aerodromes’ Movement Area

João Almeida, Gonçalo Cruz, Diogo Silva, Tiago Oliveira

2023

Abstract

This work describes a low-cost and passive system installed on ground vehicles that detects Foreign Object Debris (FOD) at aerodromes’ movement area, using neural networks. In this work, we created a dataset of images collected at an airfield to test our proposed solution, using three different electro-optical sensors, capturing images in different wavelengths: i) visible, ii) near-infrared plus visible and iii) long-wave infrared. The first sensor captured 9,497 images, the second 5,858, and the third 10,388. Unlike other works in this field, our dataset is publicly available, and was collected accordingly to our envisioned real world application. We rely on image classification, object detection networks and image segmentation networks to find objects in the image. For classifier and detector, we choose Xception and YOLOv3, respectively. For image segmentation, we tested several approaches based on Unet with backbone networks. The classification task achieved an AP of 77:92%, the detection achieved 37:49% mAP and the segmentation network achieved 26:9% mIoU.

Paper Citation

in Harvard Style

Almeida J., Cruz G., Silva D. and Oliveira T. (2023). Application of Deep Learning to the Detection of Foreign Object Debris at Aerodromes’ Movement Area. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 814-821. DOI: 10.5220/0011790600003417

in Bibtex Style

@conference{visapp23,
author={João Almeida and Gonçalo Cruz and Diogo Silva and Tiago Oliveira},
title={Application of Deep Learning to the Detection of Foreign Object Debris at Aerodromes’ Movement Area},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={814-821},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011790600003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - Application of Deep Learning to the Detection of Foreign Object Debris at Aerodromes’ Movement Area
SN - 978-989-758-634-7
AU - Almeida J.
AU - Cruz G.
AU - Silva D.
AU - Oliveira T.
PY - 2023
SP - 814
EP - 821
DO - 10.5220/0011790600003417
PB - SciTePress