Underwater biological target detection is a critical technology for enabling the automated harvesting capabilities of underwater robots [1]. To address the challenges in detecting benthic organisms in marine pastures [2], including small target sizes, mutual occlusion between targets, and misdetections or missed detections caused by low-quality marine images that make it difficult to distinguish benthic organisms from the ocean background, this study proposes ReFM-YOLO, a detection algorithm for benthic organisms in marine pastures based on an improved YOLO11n model. Firstly, to enhance the model’s ability to detect small target organisms, a novel Re-Calibration Feature Pyramid Network (Re-CalibrationFPN) is designed in the neck network; Secondly, to enhance the model’s ability to extract features from targets of different scales, a novel shared-parameter convolution module, FPSConv, is proposed to replace the original SPPF module. The FPSConv module utilizes the shared-parameter convolution operations for feature extraction, enabling the capture of finer-grained features while reducing the model’s parameter count; Finally, to enhance the model’s ability to detect occluded organisms, an occlusion-aware attention mechanism is integrated into the head, and a novel detection head, MultiSEAM-Head, is proposed. Experiments were conducted on the DUO dataset to validate the performance of the improved model. The results demonstrate that the improved model exhibits better detection performance. Compared to YOLO11n, the proposed algorithm achieves a 2.3% increase in, a 3.7% increase in:0.95, a 1.7% increase in precision (P), and a 2.4% increase in recall (R), thus validating the effectiveness of the algorithm.