Geometric relation-based feature aggregation for 3D small object detection.

Point cloud-based 3D small object detection is crucial for autonomous driving and smart ships. The current 3D object detection mainly relies on object global features derived from 3D and 2D convolutional networks, inevitably leading to the loss of substantial object detail information. As large objects contain enough points to obtain sufficient global features, they are easy to identify. In contrast, small objects contain fewer points and their global features are weak, resulting in false classifications and inaccurate location estimates. Therefore, it is necessary to take into account the local geometric relation features of the object to develop adequate discriminative features. Moreover, the current two-stage 3D object detection speed is relatively slow due to the complex refinement structure, which is adverse to real-time detection. In this paper, an efficient 3D small object detection network with two novel modules is proposed. Firstly, the Geometric relation-based Feature Aggregation (GFA) module is designed to improve small object detection performance. This module flexibly aggregates the features of voxels and original points near the key points, for key points to aggregate more local discriminate features of objects, which is conducive to small object detection. Subsequently, the Key point Feature Abstraction (KFA) module is designed to improve the speed of small object detection, through which object global features can be rapidly obtained and the detection performance can be enhanced. Experimental results show that this method achieves state-of-the-art small object detection performance on both the KITTI dataset and the River Cargo Ship dataset. [ABSTRACT FROM AUTHOR]