Research output per year
Research output per year
Project Details
Goal of the project
The aim of this study is threefold:
i. To develop an autonomous methodology for the characterization of environmental parameters to be injected in real-time into the target recognition process.
ii. To assess the operational performance of the techniques developed for target detection and recognition under the mine countermeasures capability replacement (rMCM) project.
iii. To create a database or model for the construction of Additional Military Layers to enhance environmental awareness for mine-search.
i. To develop an autonomous methodology for the characterization of environmental parameters to be injected in real-time into the target recognition process.
ii. To assess the operational performance of the techniques developed for target detection and recognition under the mine countermeasures capability replacement (rMCM) project.
iii. To create a database or model for the construction of Additional Military Layers to enhance environmental awareness for mine-search.
Funding acknowledgement
The project DAP/21-11 is financed under the DFR call.
| Status | Finished |
|---|---|
| Effective start/end date | 1/09/21 → 31/08/25 |
Collaborative partners
- Royal Military Academy (lead)
- NATO STO Center for Maritime Research and Experimentation
- University of Ghent
- ECA ROBOTICS
- ENSTA Bretagne
RHID domain
- Data acquisition and processing
Fingerprint
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Research output
- 2 Conference contribution
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A new Deep Learning Neural Network Architecture for Seafloor Characterisation
Arhant, Y., Neyt, X. & Pizurica, A., 2 Oct 2023, Proceedings of the NATO Military Sensing Symposium 2023. NATO Science and Technology Organization, 4 p. 92Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open AccessFile33 Downloads (Pure) -
D4SC: Deep Supervised Semantic Segmentation for Seabed Characterisation in Low-Label Regime
Arhant, Y., Tellez, O. L., Neyt, X. & Pizurica, A., 2023, IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 6932-6935 4 p. (International Geoscience and Remote Sensing Symposium (IGARSS); vol. 2023-July).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Open AccessFile20 Downloads (Pure)
Activities
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Towards more trustworthy deep neural networks
Arhant, Y. (Speaker)
23 May 2024Activity: Talk or presentation › Talk at a scientific Seminar
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Artificial Intelligence for Defence Seminars
Arhant, Y. (Participant)
1 Jul 2022Activity: Participating in or organising an event (conference, measurement campaign, ...) › Participation to a Seminar
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Are Deep Neural Networks Trustworthy ?
Arhant, Y. (Speaker), Lopera Tellez, O. (Co-author), Neyt, X. (Co-author) & Pizurica, A. (Co-author)
28 May 2024Activity: Talk or presentation › Oral scientific presentation