In order to improve the perception ability of Unmanned Surface Vehicles ( USV) to typical small water surface targets, cooperative environment perception method for nautical radar and photoelectric pod facing to Unmanned Surface Vehicles is studied in this paper. Firstly, Gaussian filtering and morphological filtering are used to process the image of the nautical radar; Secondly, the deep learning target detection algorithm is studied and an improved single shot multibox detector ( SSD) target detection algorithm model is proposed based on multi-scale convolution fusion structure and spatial attention enhancement. The improvement was designed to improve the feature retention of small target with weak texture. The proposed method was verified in the VOC207 data set and typical water surface scene. In addition, the paper proposes the flow chart of joint target perception with nautical radar and photoelectric pod. The perception target distribution maps of nautical radar and photoelectric pod are fused to get the final target distribution map, which includes the information such as categories, orientations, distances, etc. of the targets. The experiment shows that the improved algorithm reaches the mean average precision (mAP) of 75. 3% and maintains the real-time detection speed of 64. 4 FPS. Besides, the nautical radar and photoelectric pod can detect the sea target cooperatively and effectively.