To address the challenges posed by environmental noise, complex water surface target distributions, and the blurring of small-scale features in water surface target detection against complex river backgrounds, this paper presents UltraWS, an enhanced water surface target detection algorithm that integrates multi-scale features and attention mechanisms. Firstly, a spatial attention module and multi-head strategy are incorporated into a standard detection network to fuse multi-scale features and improve the detection capability of small targets. Secondly, the UltraLU module is introduced to enhance class activation mapping and reduce the influence of environmental and distribution factors on target detection. Finally, a Tucker tensor decomposition method is applied to achieve model lightweighting, enhancing model interpretability and inference speed. Experimental results demonstrate that the proposed UltraWS algorithm improves resistance to background noise, enhances small target detection, and achieves a balance between detection speed and accuracy suitable for edge deployment requirements. On the WSODD dataset, the algorithm achieves the highest mAP value of 84.5%, outperforming other mainstream methods by a considerable improvement. This proposed algorithm, coupled with the established channel safety inspection system and evaluation method, contributes significantly to the advancement of intelligent river transportation.