To address the problems of noise interference and missed detection of small objects in water surface object detection, this study proposes an improved You Only Look Once version 8 (YOLOv8) algorithm for water surface small object detection, namely, YOLOv8-WSSOD. Specifically, to reduce the noise interference caused by the complex water surface environment during the downsampling in the backbone network, the study proposes the C2f-BiFormer (C2fBF) module constructed based on BiFormer’s bi-level routing attention mechanism to retain fine-grained contextual feature information during feature extraction. Then, as to the missed detection of small objects on the water surface, a smaller detection head is added to enhance the network’s sensitivity to small objects. At the Neck end, the ghost-shuffle convolution (GSConv) and Slim-neck structures are used to reduce the model’s complexity and maintain precision. Finally, the limitations of the complete intersection over union (CIoU) loss function are overcome by the minimum point distance-based IoU (MPDIoU) loss function to improve the model’s detection precision. The experimental results show that compared with the original YOLOv8 algorithm, the proposed algorithm increases the mean average precision mAP@0.5 and mAP@0.5:0.95 on small objects on the water surface by 4.6% and 2.2%, respectively. Furthermore, the modified algorithm, achieving a detection speed of 86 f/s, is readily available for fast and accurate detection of small objects on the water surface.