Uncovering Anomalous Events for Marine Environmental Monitoring via Visual Anomaly Detection
Year: 2025
Type: preprint
Abstract: Underwater video monitoring is a promising strategy for assessing marine biodiversity, but the vast volume of uneventful footage makes manual inspection highly impractical. In this work, we explore the use of visual anomaly detection (VAD) based on deep neural networks to automatically identify interesting or anomalous events. We introduce AURA, the first multi-annotator benchmark dataset for underwater VAD, and evaluate four VAD models across two marine scenes. We demonstrate the importance of robust frame selection strategies to extract meaningful video segments. Our comparison against multiple annotators reveals that VAD performance of current models varies dramatically and is highly sensitive to both the amount of training data and the variability in visual content that defines "normal" scenes. Our results highlight the value of soft and consensus labels and offer a practical approach for supporting scientific exploration and scalable biodiversity monitoring.
Source: arXiv (Cornell University)
Authors: Weihl, Laura, Bengtson, Stefan H., Novak, Nejc, Pedersen, Malte
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Life below water
Open Access status: green
OpenAlex ID: W7091187193
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