The ocean archives massive, stable remote sensing datasets, and leveraging these data to achieve intelligent real-time recognition of marine organisms has become a core task in the field of marine remote sensing. However, in complex seabed environments, marine monitoring equipment is often constrained by limited computing power—this creates an urgent demand among oceanographers for detection algorithms with low computational complexity, which can be widely deployed on low-cost, simple marine remote sensing devices. To address this demand, this study proposes a deep learning-based algorithm for lightweight seabed organism detection efficiently (LSOD). This algorithm integrates Mamba and YOLO principles to enable efficient lightweight benthic organism detection. For LSOD’s neck, the original concatenation modules are improved, which efficiently aggregates feature layer information across backbone stages for cross-scale fusion. To further reduce the computational requirements of LSOD, a new detection head module based on group normalization and shared convolution operations is designed. These improvements maintain a reasonable computational load while enhancing the precision of the object detection network. EUDD tests indicate LSOD’s performance: the detection precision achieves 90.6% (sea cucumbers), 91.6% (sea urchins), and 93.5% (scallops). Comparisons with mainstream models confirm its superiority in detecting benthic organisms. This work is expected to provide new insights and approaches for intelligent remote sensing and analysis in marine ranches.
Rao et al. (2026) studied this question.