ST-MVDNet++: Improve Vehicle Detection with Lidar-Radar Geometrical Augmentation via Self-Training

Abstract

We aim to improve the performance of the vehicle detection model with Lidar-Radar fusion and data augmentation. The recent works for Lidar-Radar fusion such as MVDNet or ST-MVDNet, have been proposed to have effective performance in detecting vehicles, and address the issue regarding missing modality. However, there are few works applying some global data augmentations such as rotation, translation, and scaling which are common for Lidar-only model. In order to further improve the previous Lidar-Radar fusion model, we propose a model named ST-MVDNet++ by leveraging the self-training teacher-student framework with integrating more common data augmentations such as global rotation, translation, and scaling. To ensure the data augmentations are consistent and matched across Lidar and Radar, we apply the augmentations on bird-eye-view coordinates. We also introduce the student-only augmentation for robust training of the student model with the consistency loss from teacher model. We demonstrate that our leveraging of global consistent Lidar-Radar augmentation improve the previous works by 1 ∼ 2% in all of the experimental settings.