|
As a key part of autonomous driving technology, 3D object detection in Bird-Eye-View(BEV) view is designed to accurately detect road conditions based on multiple sensor inputs. However, current 3D detection methods are usually biased towards the acquisition of depth information. There is a strong correlation between 2D object detection and 3D object detection in BEV, especially the latest 2D detection has high robustness and accuracy in complex environments. To the end, this paper proposes a 3D object detection algorithm with object feature enhancement , which reasonably uses mask generated by 2D object detection as prior information and facilitates object extraction in BEV. In particular, in order to amplify the difference between the target and the background, we first designed the Data Preparation Module(DPM) to convert the 2D detection results into a binary target mask which completely separates the target from the background. Then, an Object Enhancement Module (OEM) is designed to enhance the contrast and texture details between the object and background. Finally, we propose Channel Interaction Module(CIM) considering the correlation between multi-channel features, which fully allocates the weights between channels. In this way, MaskBEV-Tiny scores 34.0% mAP and 41.1% NDS on the nuScenes val set which surpasses BEVDet-Tiny by a largin of 2.9% mAP and 2.0% NDS.
|
