Multi-Task Cross-Modality Attention-Fusion for 2D Object Detection
Huawei Sun Hao Feng Georg Stettinger Lorenzo Servadei Robert Wille
Accurate and robust object detection is critical for autonomous driving.
Image-based detectors face difficulties caused by low visibility in adverse
weather conditions. Thus, radar-camera fusion is of particular interest but
presents challenges in optimally fusing heterogeneous data sources. To approach
this issue, we propose two new radar preprocessing techniques to better align
radar and camera data. In addition, we introduce a Multi-Task Cross-Modality
Attention-Fusion Network (MCAF-Net) for object detection, which includes two
new fusion blocks. These allow for exploiting information from the feature maps
more comprehensively. The proposed algorithm jointly detects objects and
segments free space, which guides the model to focus on the more relevant part
of the scene, namely, the occupied space. Our approach outperforms current
state-of-the-art radar-camera fusion-based object detectors in the nuScenes
dataset and achieves more robust results in adverse weather conditions and
nighttime scenarios.