Novelty Detection in Underwater Acoustic Environments for Maritime Surveillance Using an Out-of-Distribution Detector for Neural Networks

Reliable detection of unknown signals is essential for ensuring the robustness of underwater acoustic sensing systems, particularly in maritime security and autonomous navigation. However, Conventional deep learning models often exhibit overconfidence when encountering unknown signals and are unable...

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Bibliografski detalji

Glavni autori:	Nayeon Kim, Minho Kim, Chanil Lee, Chanjun Chun, Hong Kook Kim
Format:	Artigo
Jezik:	Inglês
Izdano:	MDPI AG 2025-12-01
Serija:	Sensors
Teme:	novelty detection underwater acoustic sensing environments Monte Carlo dropout out-of-distribution detector for neural networks (ODIN) DeepShip dataset
Online pristup:	https://www.mdpi.com/1424-8220/26/1/37
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