Panoptic perception is essential to unmanned surface vehicles (USVs) for autonomous navigation. The current panoptic perception scheme is mainly based on vision only, that is, object detection and semantic segmentation are performed simultaneously based on camera sensors. Nevertheless, the fusion of camera and radar sensors is regarded as a promising method which could substitute pure vision methods, but almost all works focus on object detection only. Therefore, how to maximize and subtly fuse the features of vision and radar to improve both detection and segmentation is a challenge. In this paper, we focus on riverway panoptic perception based on USVs, which is a considerably unexplored field compared with road panoptic perception. We propose Efficient-VRNet, a model based on Contextual Clustering (CoC) and the asymmetric fusion of vision and 4D mmWave radar, which treats both vision and radar modalities fairly. Efficient-VRNet can simultaneously perform detection and segmentation of riverway objects and drivable area segmentation. Furthermore, we adopt an uncertainty-based panoptic perception training strategy to train Efficient-VRNet. In the experiments, our Efficient-VRNet achieves better performances on our collected dataset than other uni-modal models, especially in adverse weather and environment with poor lighting conditions. Our code and models are available at \url{https://github.com/GuanRunwei/Efficient-VRNet}.

通过在无人船上基于 Efficient-VRNet 的上下文聚类和视觉与4D mmWave雷达的非对称融合，我们提出了一种对河道实现同时进行检测和分割的高效全景感知模型，并采用基于不确定性的感知训练策略，在逆境天气和光照条件不佳的环境中获得了比其他单模型更好的性能。

高效VRNet: 基于视觉和4D毫米波雷达的不对称公平融合的河道全景感知的精妙融合网络