Authors:
João Almeida
;
Gonçalo Cruz
;
Diogo Silva
and
Tiago Oliveira
Affiliation:
Portuguese Air Force Academy Research Center, Sintra, Portugal
Keyword(s):
Foreign Object Debris, Computer Vision, Dataset, Image Classification, Object Detection.
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 d
etection achieved 37:49% mAP and the segmentation network achieved 26:9% mIoU.
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