MarineDet: Towards Open-Marine Object Detection
Liang Haixin Zheng Ziqiang Ma Zeyu Sai-Kit Yeung
Marine object detection has gained prominence in marine research, driven by
the pressing need to unravel oceanic mysteries and enhance our understanding of
invaluable marine ecosystems. There is a profound requirement to efficiently
and accurately identify and localize diverse and unseen marine entities within
underwater imagery. The open-marine object detection (OMOD for short) is
required to detect diverse and unseen marine objects, performing categorization
and localization simultaneously. To achieve OMOD, we present
\textbf{MarineDet}. We formulate a joint visual-text semantic space through
pre-training and then perform marine-specific training to achieve
in-air-to-marine knowledge transfer. Considering there is no specific dataset
designed for OMOD, we construct a \textbf{MarineDet dataset} consisting of 821
marine-relative object categories to promote and measure OMOD performance. The
experimental results demonstrate the superior performance of MarineDet over
existing generalist and specialist object detection algorithms. To the best of
our knowledge, we are the first to present OMOD, which holds a more valuable
and practical setting for marine ecosystem monitoring and management. Our
research not only pushes the boundaries of marine understanding but also offers
a standard pipeline for OMOD.