MarineDet: Towards Open-Marine Object Detection
Liang Haixin, Zheng Ziqiang, Ma Zeyu, Sai-Kit Yeung

TL;DR
MarineDet introduces a novel open-marine object detection framework that leverages joint visual-text semantic space and transfer learning, enabling accurate detection of diverse unseen marine objects in underwater imagery.
Contribution
This paper presents the first open-marine object detection method, MarineDet, including a new marine-specific dataset and a pipeline for detecting unseen marine entities.
Findings
MarineDet outperforms existing object detection algorithms on the MarineDet dataset.
The joint visual-text semantic space improves marine object recognition.
MarineDet establishes a new standard for marine ecosystem monitoring.
Abstract
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.…
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Taxonomy
TopicsUnderwater Acoustics Research · Coral and Marine Ecosystems Studies · Marine animal studies overview
