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Naslov:UAV Thermal Imaging for Unexploded Ordnance Detection by Using Deep Learning
Avtorji:ID Bajić, Jr., Milan (Avtor)
ID Potočnik, Božidar (Avtor)
Datoteke:.pdf Bajic-2023-UAV_Thermal_Imaging_for_Unexploded.pdf (16,94 MB)
MD5: B6BE84DBCFD47737348EDBD17C5E32C3
 
URL https://www.mdpi.com/2072-4292/15/4/967
 
Jezik:Angleški jezik
Vrsta gradiva:Članek v reviji
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FERI - Fakulteta za elektrotehniko, računalništvo in informatiko
Opis:A few promising solutions for thermal imaging Unexploded Ordnance (UXO) detection were proposed after the start of the military conflict in Ukraine in 2014. At the same time, most of the landmine clearance protocols and practices are based on old, 20th-century technologies. More than 60 countries worldwide are still affected by explosive remnants of war, and new areas are contaminated almost every day. To date, no automated solutions exist for surface UXO detection by using thermal imaging. One of the reasons is also that there are no publicly available data. This research bridges both gaps by introducing an automated UXO detection method, and by publishing thermal imaging data. During a project in Bosnia and Herzegovina in 2019, an organisation, Norwegian People's Aid, collected data about unexploded ordnances and made them available for this research. Thermal images with a size of 720 x 480 pixels were collected by using an Unmanned Aerial Vehicle at a height of 3 m, thus achieving a very small Ground Sampling Distance (GSD). One of the goals of our research was also to verify if the explosive war remnants' detection accuracy could be improved further by using Convolutional Neural Networks (CNN). We have experimented with various existing modern CNN architectures for object identification, whereat the YOLOv5 model was selected as the most promising for retraining. An eleven-class object detection problem was solved primarily in this study. Our data were annotated semi-manually. Five versions of the YOLOv5 model, fine-tuned with a grid-search, were trained end-to-end on randomly selected 640 training and 80 validation images from our dataset. The trained models were verified on the remaining 88 images from our dataset. Objects from each of the eleven classes were identified with more than 90% probability, whereat the Mean Average Precision (mAP) at a 0.5 threshold was 99.5%, and the mAP at thresholds from 0.5 to 0.95 was 87.0% up to 90.5%, depending on the model's complexity. Our results are comparable to the state-of-the-art, whereat these object detection methods have been tested on other similar small datasets with thermal images. Our study is one of the few in the field of Automated UXO detection by using thermal images, and the first that solves the problem of identifying more than one class of objects. On the other hand, publicly available thermal images with a relatively small GSD will enable and stimulate the development of new detection algorithms, where our method and results can serve as a baseline. Only really accurate automatic UXO detection solutions will help to solve one of the least explored worldwide life-threatening problems.
Ključne besede:unmanned aerial vehicle, unexploded ordnance, thermal imaging, UXOTi_NPA dataset, convolutional neural networks, deep learning
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Poslano v recenzijo:21.12.2022
Datum sprejetja članka:06.02.2023
Datum objave:09.02.2023
Založnik:MDPI
Leto izida:2023
Št. strani:Str. 1-18
Številčenje:Letn. 15, Št. 4, št. članka 967
PID:20.500.12556/DKUM-87039 Novo okno
UDK:004.5
COBISS.SI-ID:142577155 Novo okno
DOI:10.3390/rs15040967 Novo okno
ISSN pri članku:2072-4292
Datum objave v DKUM:12.02.2024
Število ogledov:389
Število prenosov:26
Metapodatki:XML DC-XML DC-RDF
Področja:Ostalo
:
BAJIĆ, JR., Milan in POTOČNIK, Božidar, 2023, UAV Thermal Imaging for Unexploded Ordnance Detection by Using Deep Learning. Remote sensing [na spletu]. 2023. Vol. 15, no. Št. 4,  članka 967, p. 1–18. [Dostopano 27 marec 2025]. DOI 10.3390/rs15040967. Pridobljeno s: https://dk.um.si/IzpisGradiva.php?lang=slv&id=87039
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Gradivo je del revije

Naslov:Remote sensing
Skrajšan naslov:Remote sens.
Založnik:MDPI
ISSN:2072-4292
COBISS.SI-ID:32345133 Novo okno

Gradivo je financirano iz projekta

Financer:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:P2-0041
Naslov:Računalniški sistemi, metodologije in inteligentne storitve

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Začetek licenciranja:09.02.2023

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:zračna plovila, neeksplodirano strelivo, globoko učenje, konvolucijske nevronske mreže, simulacija, toplotno slikanje


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