O²Exp: Online Object Exploration in Underwater Environment

The underwater environment contains a wealth of biological and mineral resources, making the deployment of autonomous underwater vehicles (AUVs) essential for exploration and development. Despite years of research in data-driven machine vision techniques, the offline collection of underwater data remains quite difficult compared to terres-trial samples. This paper focuses on online object exploration in underwater environments without manual intervention, including sub-tasks of close- and open-set detection, fine-grained novel-class subdivision, and few-shot incremental learning. To address this challenge, we start with a few-shot detector for detecting known classes and propose an open-set detector for exploring novel categories. The open-set detector can model unseen objects with fused semantics-localization cues and discrepancy-enhanced representation. Furthermore, we design detector-driven clustering to subdi-vide novel objects into an arbitrary number of novel classes as pseudo-labels. Finally, incremental learning is performed to model novel-category representation while maintaining base-class knowledge, where gradient rescaling and knowl-edge distillation strategies are designed to avoid catastrophic forgetting. Overall, our proposed framework, called O²Exp, can autonomously explore objects in unstructured underwater environments. Extensive experiments with public datasets and real-world tests verify the accuracy, robustness, and practicality of the proposed O²Exp framework.