A three-dimensional (3D) lidar is the main sensing module of an unmanned surface vehicle (USV). The interference of water clutter will reduce the energy efficiency of target detection and affect autonomous navigation’s obstacle avoidance function. Based on 3D lidar, this study proposes a surface target DBSCAN-VoxelNet joint detection algorithm. The proposed algorithm employs a noise density clustering approach (DBSCAN) to filter surface clutter interference; a depth neural network VoxelNet is employed to divide surface sparse point cloud data into voxels, and the results are input into a Hash table for efficient query; the feature tensor is extracted through the feature learning layer, and the tensor is input into the convolution layer to obtain the global target information, resulting in high-precision target detection. The experimental results reveal that the proposed joint detection algorithm performs well in suppressing clutter in the water area, with a mean average precision (mAP) of 82.4%, which effectively enhances surface target detection accuracy.