To address challenges in underwater fish detection caused by turbid water and uneven illumination—such as blurred edges,high missed-detection rates,and the high computational cost of existing models,this paper proposes CRL-YOLO11,an improved lightweight detection algorithm based on YOLOv11n. Firstly,an Edge -Aware and Context -Guided Attention Block is proposed to enhance the model ’s perception of weakly represented targets,thereby improving the detection of small fish targets. Secondly,a lightweight and efficient aggregation module is designed,which leverages a re-parameterized multi-branch structure to enable cross-scale feature fusion and reduce information loss during feature propagation. In addition,to address the issue of high computational cost,a selective channel down-sampling module and a lightweight asymmetric detection head are introduced. Results obtained on the self-built dataset demonstrate that, compared with the baseline model YOLOv11n,the proposed CRL-YOLO11 achieves a 2. 1% improvement in mAP50 and a 2. 9% increase in recall,while reducing the model's weights and parameters to 86% and 82% of the original,respectively. Furthermore,on the Kaggle-Fish dataset,the mAP50 increased by 0. 7% and recall by 2. 4%. On the URPC2019 dataset,the mAP50 improved by 1. 1%. The experimental results demonstrate that the proposed model offers a balanced trade-off between detection accuracy,generalization,and deployment efficiency,rendering it well - suited for real-time underwater object detection in smart fisheries and aquaculture environments.