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.