To improve the perception ability of typical small water targets for unmanned surface vehicle (USV), this paper proposes an improved SSD object detection algorithm based on multi-scale convolution layer fusion and spatial attention enhancement architecture. Firstly, a multi-scale fusion method is utilized to improve the semantic representation of SSD shallow layer for small targets. Secondly, the spatial attention architecture is designed for each convolutional feature extraction layer to improve feature retention of small targets with weak texture. Finally, the proposed algorithm is evaluated on VOC and self-constructed surface target dataset. The real sea target detection and identification verification based on USV are carried out. Experimental results show that the proposed method can reach high operating efficiency with 15 fps on the USV Nvidia platform. The targets, such as buoys, bridge piers, fishing boats, speed boats and cargo ships, can be identified accurately. Compared with the original SSD algorithm, the proposed method could achieve a higher detection rate for small targets in the typical sea scene, which is increased by nearly 20. 2% when the false alarm rate is 5% . The average effective detection rate can reach 79. 3% .