Rapid identification and precise positioning of surrounding targets are prerequisites and represent the foundation for safe autonomous vehicle driving. A point cloud 3D object detection algorithm based on an improved SECOND algorithm is proposed to address the challenges of inaccurate recognition and positioning in voxel-based point cloud 3D object detection methods. First, an adaptive spatial feature fusion module is introduced into a 2D convolutional backbone network to fuse spatial features of different scales, so as to improve the model's feature expression capability. Second, by fully utilizing the correlation between bounding box parameters, the three-dimensional distance-intersection over union (3D DIoU) is adopted as the bounding box localization regression loss function, thus improving regression task efficiency. Finally, considering both the classification confidence and positioning accuracy of candidate boxes, a new candidate box quality evaluation standard is utilized to obtain smoother regression results. Experimental results on the KITTI test set demonstrate that the 3D detection accuracy of the proposed algorithm is superior to many previous algorithms. Compared with the SECOND benchmark algorithm, the car and cyclist classes improves by 2.86 and 3.84 percentage points, respectively, under simple difficulty; 2.99 and 3.89 percentage points, respectively, under medium difficulty; and 7.06 and 4.27 percentage points, respectively, under difficult difficulty.