3D target detection based on dynamic occlusion processing

In order to solve the multi-vehicle mutual occlusion problem encountered in 3D target detection by self-driving vehicles, this paper proposes a monocular 3D detection method that includes dynamic occlusion determination. The method adds a dynamic occlusion processing module to the CenterNet3D network framework to improve the accuracy of 3D target detection of occluded vehicles in the road. Specifically, the occlusion determination module of the method uses the 2D detection results extracted from target detection as the occlusion relationship determination condition, wherein the method of changing the occlusion determination threshold with the depth value is introduced. Then the occlusion compensation module is used to compensate and adjust the 3D detection results of the occurring occluded vehicles, and finally the 3D target detection results are output. The experimental results show that the method improves the accuracy of both vehicle center point detection and 3D dimensional detection results in the case of long-distance continuous vehicle occlusion. And compared with other existing methods, the accuracy of 3D detection results and bird's-eye view detection results are improved by 1%-2.64% in the case of intersection over union of 0.5. The method can compensate for the occluded vehicles in 3D target detection and improve the accuracy