arXiv:2607.04453v1 Announce Type: cross Abstract: The assessment of planktonic standing stocks and microorganism structures is critical for understanding upper ocean biological processes. Currently, autonomous underwater vehicles (AUVs) equipped with in-situ optical imaging and artificial intelligence (AI) methods offer a promising solution for persistent surveillance, mapping and monitoring of planktonic life. However, current AI methods often lack robustness in dynamic, unstructured environments, where environmental noise and non-biological artifacts lead to frequent misclassifications. Stan
The proliferation of AI methods in critical applications like environmental monitoring is highlighting the urgent need for robustness verification in dynamic, unstructured real-world environments.
Ensuring the reliability and accuracy of AI systems in autonomous platforms is crucial for their effective deployment in scientific research and defence, preventing misclassifications that could undermine data integrity and operational success.
This research provides a framework for assessing and improving the robustness of AI-driven classification systems in autonomous underwater vehicles, potentially accelerating their reliable adoption in diverse applications.
- · Autonomous Underwater Vehicle (AUV) manufacturers
- · Marine biology research
- · Environmental monitoring agencies
- · AI robustness verification platforms
- · AI models lacking robustness
- · Traditional manual plankton classification methods
Improved reliability and trust in AI-powered autonomous systems for environmental data collection.
Expansion of autonomous underwater vehicle applications into more complex and critical monitoring tasks due to enhanced AI performance.
Reduced need for human intervention in vast oceanic data collection, leading to cost efficiencies and broader coverage of marine ecosystems.
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Read at arXiv cs.AI