To address the accuracy and robustness issues of small floating object detection on water surfaces under complex scenarios such as wave disturbances, changes in lighting, and partial occlusion by floating debris, the MEC-YOLOv11n algorithm is proposed. The MEC-YOLOv11n algorithm consists of three parts: Backbone, Neck and Head. To increase the recognition area of the target receptive field, we designed the MSWTC structure and improved the C3k2 structure in the Neck part, which significantly enhances the extraction ability of small floating objects on water surfaces, thus strengthens the model′s ability to capture details in complex backgrounds; next, we proposed a EUCB up-sampling method, replacing the traditional up-sampling module in v11, which enhances the clarity of image edges during up-sampling, making the object contours more accurate in high-resolution feature maps, especially when dealing with complex backgrounds and small target detection tasks, which significantly improves the model′s ability to capture details; finally, we designed an attention module CCA specifically for recognizing edge features before the Head, further optimizing the model′s performance in edge information extraction. Experimental results show that after optimization, the precision P of the model has increased by 3.3%, the recall R has increased by 2.4%, the mAP50 has increased by 2.5%, and the mAP50.95 has increased by 1.5%.