To improve the performance of environment perception of autonomous vehicles and enhance the ability of monocular cameras to perceive obstacle 3D and boundary information, this paper proposed a ground-aware 3D object detection algorithm. Based on an optimized CenterNet model, it used DLA as the backbone network to increase the redundant information prediction of 3D bounding box center. According to the ground-aware information of the autonomous driving scenario and combined with the pinhole camera model, it obtained the object 3D center depth information to optimize the deep network learning effect. This paper evaluated the algorithm performance using the KITTI 3D dataset. The results show that the algorithm runs at 20 fps on the basis of ensuring the accuracy of 2D object detection, which meets the real-time requirements of autonomous driving perception. At the same time, compared with the CenterNet model, there are performance improvements of 4.4 and 4.4% in the average orientation score and the bird eye view AP, respectively. Thus, the algorithm can improve the ability of autonomous vehicles to perceive obstacle 3D and boundary information.

[2022-01-05] Printed Article /doc/2021.05.0205/v2