Abstract: In recent years, the realm of laser radar target detection has increasingly drawn inspiration from network designs employed in image target detection. Nevertheless, persistent challenges encompass inefficient computations impeding real-time applications and suboptimal performance resulting from overly simplistic network structures. The proposed network embraces a refined design characterized by a single-stage, anchor-free methodology. It strategically enhances the voxelization representation of laser point clouds, thereby bolstering computational efficiency while preserving crucial elevation features. Leveraging the principles of residual networks, a deeper backbone network structure is crafted to adeptly extract deep features. Additionally, the incorporation of a feature pyramid enhances the detection capabilities, especially for smaller targets. Across various object categories, the proposed network demonstrates outstanding performance on the KITTI public dataset, as reflected in the mAP indicator with 1% to 3% improvement. Runtime assessments on an autonomous driving computing platform have a processing speed of 43 ms per frame. validating the network’s ability to meet real-time requirements.