Object detection on the ground battlefield is the basis of precise strike, playing a vital role in modern unmanned warfare. The traditional image algorithm is restricted by lighting, weather and other conditions, which can be solved by 3D detection algorithm using lidar. For unmanned vehicles’ detection task on land battlefield, a 3D detection algorithm based on convolutional neural network is proposed in this paper. By optimizing the feature fusion module of VoxelNet, a group of end-to-end efficient networks are designed, and a non-maximum suppression strategy based on distance is improved. Experiments show that on the self-built dataset, original VoxelNet’s AP of vehicle target is 78.53%, while our network performance is 84.11%, which has great value for 3D detection task in the feature military field.