Object detection plays a crucial role in practical applications such as robotics and autonomous driving. In these scenarios, real-time execution of object detection tasks on resource-constrained platforms is essential, demanding highly on parameters and detection speed of object detection algorithm, realizing lightweight and efficient object detection algorithms. However, traditional convolutional neural networks (CNNs) with complex network structures and high computational requirements are not suitable for deployment on mobile devices. To address these challenges, this paper proposes a one-stage lightweight object detection algorithm, named CYM-Net, based on point cloud data. The CYM-Net model cleverly integrates the design principles of MobileNetV3′s bneck module and YOLOv4′s object detection concept while improving the feature pyramid, resulting in a significant reduction in model parameters. The CYM-Net model is trained and validated on the KITTI dataset. Experimental results demonstrate that the CYM-Net model outperforms other methods in both bird′s-eye view and 3D detection tasks, and it also exhibits superior detection speed. This research provides an efficient and lightweight solution for object detection in fields like robotics and autonomous driving.