This study aims to enhance the extraction capabilities of point cloud features in LiDAR object detection and improve the accuracy of object detection. This study proposes a multi-stage feature extraction method target detection for object detection based on PointPillars. First, the maximum pooling in the point cloud feature encoding target detection module is improved by introducing combined pooling to minimize spatial information loss during the pooling process. Additionally, a global attention mechanism is introduced to enhance the ability of the pseudo-image feature to express information. Finally, a cavity convolution module is integrated into the column feature extraction network to increase the receptive field and further improve the feature extraction capability. Experimental results on the KITTI data set show that the average detection accuracy of the proposed method improves by 2.91 percentage points compared with the original network. Moreover, the inference speed reaches 17.08 frame/s, which meets the requirements for real-time detection in autonomous driving.