To address the challenges of low detection accuracy and weak small-target recognition capability for Chinese sturgeon in underwater sonar images caused by noise interference, this study proposes an improved YOLOv8n-based sonar detection model. The methodology integrates three key innovations: a hierarchical attention fusion block module is designed to achieve adaptive fusion of local and global features through a hierarchical attention mechanism; a pinwheel-shaped convolution module is introduced, employing a multi-branch convolution fusion strategy with a Gaussian-distributed receptive field to enhance differential feature extraction for small targets; a lightweight shared convolutional separated BN (batch normalization) detection head is developed to balance detection accuracy and inference efficiency. Experiments are conducted on a self-constructed Chinese sturgeon sonar dataset, partitioned into training, validation, and test sets at an 8∶1∶1 ratio. Experimental results demonstrate that compared to the baseline YOLOv8n model, the proposed approach improves mAP@0.5 by 1.0%, precision by 2.6%, and recall by 0.3%, while reducing parameters by 0.58×106 and floating pointoperations by 0.7 GFLOPs. Ablation studies confirm that the synergistic integration of the three modules significantly enhances model performance. Comparative experiments show that the proposed model outperforms mainstream object detection models in detection accuracy under stable parameter complexity. The improved model achieves notable enhancements in both detection precision and efficiency for Chinese sturgeon in complex underwater noise environments, providing an effective technical solution for underwater sonar behavior monitoring. This work holds significant application value for Chinese sturgeon conservation, wild training in artificial breeding, and population restoration efforts.