  Despite the recent advances of deep neural networks, object detection for
adverse weather remains challenging due to the poor perception of some sensors
in adverse weather. Instead of relying on one single sensor, multimodal fusion
has been one promising approach to provide redundant detection information
based on multiple sensors. However, most existing multimodal fusion approaches
are ineffective in adjusting the focus of different sensors under varying
detection environments in dynamic adverse weather conditions. Moreover, it is
critical to simultaneously observe local and global information under complex
weather conditions, which has been neglected in most early or late-stage
multimodal fusion works. In view of these, this paper proposes a Global-Local
Attention (GLA) framework to adaptively fuse the multi-modality sensing
streams, i.e., camera, gated camera, and lidar data, at two fusion stages.
Specifically, GLA integrates an early-stage fusion via a local attention
network and a late-stage fusion via a global attention network to deal with
both local and global information, which automatically allocates higher weights
to the modality with better detection features at the late-stage fusion to cope
with the specific weather condition adaptively. Experimental results
demonstrate the superior performance of the proposed GLA compared with
state-of-the-art fusion approaches under various adverse weather conditions,
such as light fog, dense fog, and snow.
