Aiming at the low detection precision of small objects and noise interference in light detection and ranging (LiDAR) point cloud 3D object detection based on deep learning,a 3D point cloud object detection method CSA-RCNN（cross self-attention region cnn）based on cross self-attention mechanism was proposed.The cross self-attention was used to learn the coordinates and features of the point cloud simultaneously,and a multi-scale fusion (MF) module was designed to adaptively capture multi-scale features at each level.In addition,an overlapping sampling strategy was designed to selectively resample the target region of interest to obtain more foreground points,effectively reducing noise sampling.The algorithm performance test was carried out on the widely used KITTI dataset.The results show that the detection precision of the method in this paper for small objects such as pedestrians is greatly improved,and the average precision mean value is increased compared with four classical algorithms such as PointRCNN,which significantly improves the performance of 3D point cloud object detection.