Robustness Verification of an Autonomous Underwater Vehicle-based Plankton Classifier
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. Standard convolutional neural network (CNN) classifiers often struggle with such conditions, leading to misclassifications that require time-consuming ...
| Authors | Abdelrahman Sayed Sayed, Pierre-Jean Meyer, Asgeir J. Sørensen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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