Underwater biological target detection technology is of great significance to the development of numerous fields, such as marine ecological protection and fishery resource exploitation. To improve the accuracy and speed of underwater biological detection and address the high parameter count in existing models, a lightweight algorithm, YOLOv11-PiA, based on improved YOLOv11, is proposed. Firstly, the C3k2 module is improved by introducing partial convolution (PConv), resulting in the C3k2_PConv module, which effectively reduces the model’s parameter count and computational complexity while enhancing detection accuracy and speed. Secondly, the C2PSA_iRMB module, which combines the C2PSA module with the inverted residual block attention mechanism (iRMB), is proposed. This module strengthens feature information at multiple depths to enhance feature representation, thereby improving detection accuracy. Finally, the traditional convolutional downsampling module in the baseline model is replaced with the lightweight downsampling module (ADown), further reducing the model’s parameter count and improving detection efficiency. Experimental results on the RUOD and DUO underwater biological datasets show that the proposed YOLOv11-PiA model achieves mAP@0.5 values of 85.6% and 82.6%, with frames per second (FPS) reaching 97 and 116 fps, respectively. Compared with the original YOLOv11n model, YOLOv11-PiA increases mAP@0.5 by 2.8% and 2.3%, and improves FPS by 9 and 13 fps, respectively. Meanwhile, the parameter count, floating-point operations per second (FLOPs), and model size are reduced by 29.8%, 22.2%, and 29.1%, respectively. The comprehensive performance of YOLOv11-PiA outperforms mainstream target detection models such as RTDETR and YOLOv12, enabling fast and accurate detection of underwater organisms.