Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images
| dc.contributor.author | Geiger, Marco | |
| dc.contributor.author | Martin, Dominik | |
| dc.contributor.author | Kühl, Niklas | |
| dc.date.accessioned | 2022-12-27T18:55:26Z | |
| dc.date.available | 2022-12-27T18:55:26Z | |
| dc.date.issued | 2023-01-03 | |
| dc.description.abstract | The aftermath of air raids can still be seen for decades after the devastating events. Unexploded ordnance (UXO) is an immense danger to human life and the environment. Through the assessment of wartime images, experts can infer the occurrence of a dud. The current manual analysis process is expensive and time-consuming, thus automated detection of bomb craters by using deep learning is a promising way to improve the UXO disposal process. However, these methods require a large amount of manually labeled training data. This work leverages domain adaptation with moon surface images to address the problem of automated bomb crater detection with deep learning under the constraint of limited training data. This paper contributes to both academia and practice (1) by providing a solution approach for automated bomb crater detection with limited training data and (2) by demonstrating the usability and associated challenges of using synthetic images for domain adaptation. | |
| dc.format.extent | 10 | |
| dc.identifier.doi | 10.24251/HICSS.2023.103 | |
| dc.identifier.isbn | 978-0-9981331-6-4 | |
| dc.identifier.other | 94fb84e6-56b1-4278-9cee-f65eb47d276c | |
| dc.identifier.uri | https://hdl.handle.net/10125/102732 | |
| dc.language.iso | eng | |
| dc.relation.ispartof | Proceedings of the 56th Hawaii International Conference on System Sciences | |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Big Data and Analytics: Pathways to Maturity | |
| dc.title | Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images | |
| dc.type.dcmi | text | |
| prism.startingpage | 825 |
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