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Underwater biological target detection is a crucial technology for achieving automation in underwater robotic fishing. Aiming to address issues such as object overlap, occlusion, and false detections, missed detections caused by small object scales in underwater biological object detection tasks, an underwater biological object detection algorithm, FDC-YOLO v8 was proposed based on an improved YOLO v8n. Firstly, the FDC module was incorporated, which utilized deformable convolution networks in the backbone network to enhance the model’s feature extraction capability and enrich the diversity of extracted features. Secondly, the FrSAConv module, integrating fractional Fourier transform and spatial attention mechanism, was introduced to further separate diverse object features and enhance the model’s perceptual ability towards various features. Finally, the Wise-IoU loss function was introduced as the bounding box loss function to better address issues related to object imbalance and scale differences. The experiments were conducted by using the RUIE dataset, which included four types of underwater organisms: echinus, starfish, holothurian, and scallops. Experimental results demonstrated that the improved FDC-YOLO v8 achieved an mAP of 85.3%, a 2.6 percentage points improvement over the baseline model. The inference speed can reach 769 frames per second, showcasing better performance in underwater object detection of marine organisms with challenged such as object overlap, occlusion, and small-scale objects.