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An Acoustic and Optical Dataset for the Perception of Underwater Unexploded Ordnance (UXO)

Authors: Dahn, Nikolas; Bande Firvida, Miguel; Sharma, Proneet; Mohrmann, Jochen; Geisler, Oliver; Sanghamreddy, Prithvi Kumar; Marquardt, Kevin; +1 Authors

An Acoustic and Optical Dataset for the Perception of Underwater Unexploded Ordnance (UXO)

Abstract

We present a dataset for acoustic and optical sensing of unexploded ordnance (UXO) underwater. UXO in the sea pose an environmental problem and a challenge for the growing offshore economy. It is best practice to perform the recovery of ammunition without explosions to protect anthropogenic structures and marine mammals. During explosive ordnance disposal (EOD), experts often rely on optical images. However, visibility underwater may be limited in harbor areas, after storm events or in waters with very mobile sediments. Thus, visual inspection is not always possible. EOD experts therefore use high-frequency sonars with large vertical apertures like the ARIS Explorer 3000 for acoustic imaging. While efforts have been made to use the available information for 3D reconstruction, existing solutions can be limited to predefined motion patterns. The topic is inherently sensitive, and most of the data is acquired by and for private companies and not made available to the public, which impedes research in this area. Additionally, in-situ data often lacks sufficient pose information. To facilitate further research, we created a validation dataset that was recorded in a controlled experimental environment. It has the following properties: Close to 100 recordings of 3 different UXO. More than 74000 matched and annotated imaging sonar and camera frames. UXO ground truths in the form of photogrammetric 3D models. Precise position and attitude sensor data with respect to the targets. Realistic motion trajectories achievable in non-experimental environments. This dataset allows quantitative analysis with different algorithms. 3D models and trajectories can be compared against each other to evaluate different solutions. The accompanying paper is: @INPROCEEDINGS{dahn2024uxo, author={Dahn, Nikolas and Firvida, Miguel Bande and Sharma, Proneet and Christensen, Leif and Geisle, Oliver and Mohrmann, Jochen and Frey, Torsten and Kumar Sanghamreddy, Prithvi and Kirchner, Frank}, booktitle={OCEANS 2024 - Halifax}, title={An Acoustic and Optical Dataset for the Perception of Underwater Unexploded Ordnance (UXO)}, year={2024}, doi={10.1109/OCEANS55160.2024.10754316}} The paper is available on researchgate. Notes: Labels have been (unfortunately) generated for the SD camera frames. To get the correct coordinates on the included FHD images, multiply all coordinates by 3. Files: data_export_recordings.7z: main dataset data_export_polar.7z: contains only the polar-transformed sonar frames data_export_3dmodels.7z: 3d models of the UXO data_processed.7z: extracted and cut unmatched raw data

Changelog v1.1: Added sample.7z, a small excerpt from the recordings, including polar frames and labels

Keywords

Unexploded Ordnance, Environment Pollution, UXO, Imaging Sonar

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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