Aiming at the problems of lack of satellite positioning signal， limited communication and weak ambient light of Unmanned Surface Vehicle （USV） in subterranean closed water body， a cooperative visual positioning method of multiple USVs in subterranean closed water body was proposed. Firstly， a vehicle-borne light source cooperative marker was designed， and the marker structure was optimized according to the vehicle structure and application scene. Secondly， monocular vision was used to collect the marker images， and the image coordinates of the feature points were solved. Thirdly， on the basis of camera imaging model， by using the relationship between the spatial coordinates of feature points of the markers and the corresponding image coordinates， the relative positions between adjacent vehicles were calculated through improving direct linear transformation method. Fourthly， the cameras of the front and rear vehicles were used to make look face to face between the vehicles. Through the minimum variance algorithm， the relative positions calculated on the basis of the camera images of the front and rear vehicles were fused to improve the relative positioning accuracy. Finally， the absolute location of each USV was obtained by using the known absolute coordinates in the scene. The factors influencing positioning error were analyzed through simulation， and the proposed method was compared with the traditional direct linear transformation method. The results show that as the distance increases， the effect of this method becomes more obvious. At a distance of 15 m， the position variance solved by the proposed method is stable within 0.2 m²， verifying the accuracy of this method. Static experimental results show that the proposed method can stabilize the relative error within 10.0%； dynamic experimental results in underground river courses show that the absolute positioning navigation trajectory solved by the proposed method achieves accuracy similar to satellite positioning， which verifies the feasibility of this method.

During the operation of the Unmanned Surface Vehicles （USVs）， the propellers are easily gotten entangled by waterweeds， which is a problem encountered by the whole industry. Concerning the global distribution， dispersivity， and complexity of the edge and texture of waterweeds in the water surface images， the U-Net was improved and used to classify all pixels in the image， in order to reduce the feature loss of the network， and enhance the extraction of both global and local features， thereby improving the overall segmentation performance. Firstly， the image data of waterweeds in multiple locations and multiple periods were collected， and a comprehensive dataset of waterweeds for semantic segmentation was built. Secondly， three scales of input images were introduced into the network to enable full extraction of the features via the network， and three loss functions for the upsampled images were introduced to balance the overall loss brought by the three different scales of input images. In addition， a hybrid attention module， including the dilated convolution branch and the channel attention enhancement branch， was proposed and introduced to the network. Finally， the proposed network was verified on the newly built waterweed dataset. Experimental results show that the accuracy， mean Intersection over Union （mIoU） and mean Pixel Accuracy （mPA） values of the proposed method can reach 96.8%， 91.22% and 95.29%， respectively， which are improved by 4.62 percentage points， 3.87 percentage points and 3.12 percentage points compared with those of U-Net （VGG16） segmentation method. The proposed method can be applied to unmanned surface vehicles for detection of waterweeds， and perform the corresponding path planning to realize waterweed avoidance.