Abstract:

A DV-PointPillars 3D object detection model based on dual pooling attention mechanism and vertical feature fusion was proposed to improve the issues of insufficient pillar feature representation ability and false/missed detection in pillar-based 3D object detection methods for point clouds. The max and average dual pooling attention mechanism was introduced into the encoding network. By utilizing both max pooling attention and average pooling attention mechanisms, this approach can fully leverage the point cloud information within pillars, thereby the representation ability of pillar features was improved. A vertical region feature generation network was designed to obtain the feature information of the pillars in the vertical direction, and the features were fused in the backbone network to improve the information compression problem caused by the encoding method, reduce misjudgment and improve the recognition ability of occlusion. Experiments were conducted on three categories of cars, pedestrians and cyclists using the KITTI dataset from three levels of difficulty: simple, medium and difficult. The results show that: compared with the PointPillars model, the average 3D detection average precision of the DV-PointPillars model for the three categories of vehicles, pedestrians, and cyclists increased by 4.02%, 5.17%, and 5.09% respectively after adding three modules, which verifies the effectiveness of the proposed method.

Key words: autonomous driving, environmental perception, 3D object detection, point cloud, attention pooling