A System for the Automatic Detection and Evaluation of the Runway Surface Cracks Obtained by Unmanned Aerial Vehicle Imagery Using Deep Convolutional Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F26%3A00560319" target="_blank" >RIV/60162694:G43__/26:00560319 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2076-3417/13/10/6000" target="_blank" >https://www.mdpi.com/2076-3417/13/10/6000</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app13106000" target="_blank" >10.3390/app13106000</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A System for the Automatic Detection and Evaluation of the Runway Surface Cracks Obtained by Unmanned Aerial Vehicle Imagery Using Deep Convolutional Neural Networks

Popis výsledku v původním jazyce

The timely detection and recognizing of distress on an airport pavement is crucial for safe air traffic. For this purpose, a physical inspection of the airport maneuvering areas is regularly carried out, which might be time-consuming due to its size. One of the modern approaches to speeding up this process is unmanned aerial vehicle imagery followed by an automatic evaluation. This study explores the automatic detection of the transverse crack, its dimension measurement, and position determination within the slab on the concrete runway. The aerial image data were obtained from flights at the given altitude above the runway and processed using commercial multi-view reconstruction software to create a dataset for the training, verification, and testing of a YOLOv2 object detector. Once the crack was detected, the main features were obtained by image segmentation and morphological operations. The YOLOv2 detector was tuned with 3279 images until the detection metrics (average precision AP = 0.89) reached sufficient value for real deployment. The detected cracks were further processed to determine their position within the concrete slab, and their dimensions, i.e., length and width, were measured. The automated crack detection and evaluation system developed in this study was successfully verified on the experimental section of the runway as an example of practical application. It was proven that unmanned aerial vehicle imagery is efficient over broad areas and produces impressive results with the combination of artificial intelligence.

Název v anglickém jazyce

A System for the Automatic Detection and Evaluation of the Runway Surface Cracks Obtained by Unmanned Aerial Vehicle Imagery Using Deep Convolutional Neural Networks

Popis výsledku anglicky

The timely detection and recognizing of distress on an airport pavement is crucial for safe air traffic. For this purpose, a physical inspection of the airport maneuvering areas is regularly carried out, which might be time-consuming due to its size. One of the modern approaches to speeding up this process is unmanned aerial vehicle imagery followed by an automatic evaluation. This study explores the automatic detection of the transverse crack, its dimension measurement, and position determination within the slab on the concrete runway. The aerial image data were obtained from flights at the given altitude above the runway and processed using commercial multi-view reconstruction software to create a dataset for the training, verification, and testing of a YOLOv2 object detector. Once the crack was detected, the main features were obtained by image segmentation and morphological operations. The YOLOv2 detector was tuned with 3279 images until the detection metrics (average precision AP = 0.89) reached sufficient value for real deployment. The detected cracks were further processed to determine their position within the concrete slab, and their dimensions, i.e., length and width, were measured. The automated crack detection and evaluation system developed in this study was successfully verified on the experimental section of the runway as an example of practical application. It was proven that unmanned aerial vehicle imagery is efficient over broad areas and produces impressive results with the combination of artificial intelligence.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

21100 - Other engineering and technologies

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

APPLIED SCIENCES-BASEL

ISSN

2076-3417

e-ISSN

2076-3417

Svazek periodika

13

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

25

Strana od-do

6000

Kód UT WoS článku

000995662600001

EID výsledku v databázi Scopus

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