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Title:UAV Thermal Imaging for Unexploded Ordnance Detection by Using Deep Learning
Authors:ID Bajić, Jr., Milan (Author)
ID Potočnik, Božidar (Author)
Files:.pdf Bajic-2023-UAV_Thermal_Imaging_for_Unexploded.pdf (16,94 MB)
MD5: B6BE84DBCFD47737348EDBD17C5E32C3
 
URL https://www.mdpi.com/2072-4292/15/4/967
 
Language:English
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract: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.
Keywords:unmanned aerial vehicle, unexploded ordnance, thermal imaging, UXOTi_NPA dataset, convolutional neural networks, deep learning
Publication status:Published
Publication version:Version of Record
Submitted for review:21.12.2022
Article acceptance date:06.02.2023
Publication date:09.02.2023
Publisher:MDPI
Year of publishing:2023
Number of pages:Str. 1-18
Numbering:Letn. 15, Št. 4, št. članka 967
PID:20.500.12556/DKUM-87039 New window
UDC:004.5
ISSN on article:2072-4292
COBISS.SI-ID:142577155 New window
DOI:10.3390/rs15040967 New window
Publication date in DKUM:12.02.2024
Views:389
Downloads:26
Metadata:XML DC-XML DC-RDF
Categories:Misc.
:
BAJIĆ, JR., Milan and POTOČNIK, Božidar, 2023, UAV Thermal Imaging for Unexploded Ordnance Detection by Using Deep Learning. Remote sensing [online]. 2023. Vol. 15, no. Št. 4,  članka 967, p. 1–18. [Accessed 27 March 2025]. DOI 10.3390/rs15040967. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=87039
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Record is a part of a journal

Title:Remote sensing
Shortened title:Remote sens.
Publisher:MDPI
ISSN:2072-4292
COBISS.SI-ID:32345133 New window

Document is financed by a project

Funder:ARRS - Slovenian Research Agency
Project number:P2-0041
Name:Računalniški sistemi, metodologije in inteligentne storitve

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:09.02.2023

Secondary language

Language:Slovenian
Keywords:zračna plovila, neeksplodirano strelivo, globoko učenje, konvolucijske nevronske mreže, simulacija, toplotno slikanje


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