A 3D object detection method based on improved PointPillars model is proposed to address the problem of poor detection performance of small objects in current point cloud based 3D object detection algorithms. First, the pillar feature network in the PointPillars model is improved, and a new pillar encoding module is proposed. Average pooling and attention pooling are introduced into the encoding network, fully considering the local detailed geometric information of each pillar module, which improve the feature representation ability of each pillar module and further improve the detection performance of the model on small targets. Second, based on ConvNeXt, the 2D convolution downsampling module in the backbone network is improved to enable the model extract rich context semantic information and global features during feature extraction process, thus enhancing the feature extraction ability of the algorithm. The experimental results on the public dataset KITTI show that the proposed method has higher detection accuracy. Compared with the original network, the improved algorithm has an average detection accuracy improvement of 3.63 percentage points, proving the effectiveness of the method.