Abstract: 3D object detection is an important research direction of computer vision, and has a wide range of applications in areas such as autonomous driving. Existing cutting-edge works use end-to-end deep learning methods. Although it has achieved good detection results, it has problems such as high algorithm complexity, large calculation volume, and insufficient real-time performance. After analysis, we found that the deep learning method is not suitable for solving “partial tasks” in 3D object detection. For this reason, this paper proposes a 3D object detection scheme based on heterogeneous methods. This method uses both deep learning and traditional algorithms in the detection process, and divides the detection process into multi-task stages: 1)Use deep learning methods to obtain information such as the mask and object category of the detected object from the detected picture; 2) Based on the mask, use the fast clustering method to filter out the surface radar points of the target object from the radar point cloud space; 3) Use the information such as the object’s mask, category and radar point cloud to calculate the object’s orientation, border and other information to finally realize 3D object detection. We have implemented this method systematically, which we call HA3D (a heterogeneous approach for 3D object detection). Experiments show that on the 3D detection data set KITTI for cars, the method in this paper is within the acceptance range of detection accuracy decline (2.0%) compared with the representative 3D object detection method based on deep learning, the speed is increased by 52.2%. The ratio of the accuracy to the calculation time has increased by 49%. From the perspective of comprehensive performance, this method has obvious advantages.