在水产养殖中，精确实时计数鱼苗对于优化饲喂策略和提升养殖效率至关重要。然而，高密度鱼群的重叠、复杂背景和实时性要求使得传统计数方法存在显著局限性。为解决这一问题，该研究提出了一种基于改进YOLOv8s的鱼苗自动计数方法。通过在主干网络中引入通道先验卷积注意力（CPCA）机制，动态分配通道和空间维度的注意力权重，提高特征提取和目标识别能力。同时，设计轻量化计数检测头（EffiCount Head），结合耦合设计与部分卷积技术，降低模型复杂度并提升推理速度。结果显示，改进模型在鱼苗计数任务中的平均精确率达到98.2%，相比原模型提升了4.0%，参数量减少了13.6%，推理速度提升了15.8%。该方法在复杂背景和高密度场景下具备较高的精确率和鲁棒性，能够高效实现实时鱼苗计数，显著提升水产养殖的生产效率。

Accurate real-time fish fry counting is critical in aquaculture to optimize feeding strategies and enhance farming efficiency. However, traditional counting methods face significant limitations due to overlapping high-density fish schools, complex backgrounds, and real-time requirements. To address this challenge, this study proposes an automated fish fry counting method based on an enhanced YOLOv8s model. The study incorporates a Channel Prior Convolution Attention (CPCA) mechanism into the backbone network, dynamically allocating attention weights across both channel and spatial dimensions to enhance feature extraction and target recognition. Additionally, the lightweight counting detection head (EffiCount Head) is designed by integrating coupling design and partial convolution techniques to reduce model complexity and improve inference speed. The results show that the enhanced model achieves a mAP of 98.2% in fish fry counting, a 4.0% improvement over the original model. The model also reduces the number of parameters by 13.6% and increases inference speed by 15.8%. This method demonstrates high precision and robustness in complex backgrounds and high-density scenarios, enabling efficient real-time fish fry counting and significantly improving aquaculture productivity.