To address the problems of low detection accuracy and large localization errors for small water-floating garbage targets under stacked occlusion, water surface fluctuations, and illumination variation, this study proposes a small-target water-floating garbage detection model named MFPF-YOLOv11s. Firstly, a multi-stage dilated residual module is designed to suppress interfering features caused by occluded targets through multi-scale network filtering, thereby enhancing the extraction of subtle garbage features. Secondly, the Meta-ACON activation function is introduced to improve the robustness of the model in capturing the nonlinear characteristics of water-floating garbage. Next, a Context Aggregation attention mechanism is embedded into the neck to guide the network to focus more effectively on garbage target regions. Finally, the bounding box regression loss is replaced with Wise-IoU. By exploiting its dynamic non-monotonic focusing mechanism, the proposed model further improves detection accuracy and localization precision. Experimental results on the Yellow River water-floating garbage dataset demonstrate that, compared with the baseline model, MFPF-YOLOv11s improves precision, recall, and mAP50 by 3.1, 3.2, and 2.3%, respectively, with only 0.06 M additional parameters and 0.7G additional FLOPs. These results indicate that the proposed method can meet the requirements of high-precision water-floating garbage detection and provide technical support for the intelligent monitoring and management of aquatic environments.