  Noise has always been nonnegligible trouble in object detection by creating
confusion in model reasoning, thereby reducing the informativeness of the data.
It can lead to inaccurate recognition due to the shift in the observed pattern,
that requires a robust generalization of the models. To implement a general
vision model, we need to develop deep learning models that can adaptively
select valid information from multi-modal data. This is mainly based on two
reasons. Multi-modal learning can break through the inherent defects of
single-modal data, and adaptive information selection can reduce chaos in
multi-modal data. To tackle this problem, we propose a universal
uncertainty-aware multi-modal fusion model. It adopts a multi-pipeline loosely
coupled architecture to combine the features and results from point clouds and
images. To quantify the correlation in multi-modal information, we model the
uncertainty, as the inverse of data information, in different modalities and
embed it in the bounding box generation. In this way, our model reduces the
randomness in fusion and generates reliable output. Moreover, we conducted a
completed investigation on the KITTI 2D object detection dataset and its
derived dirty data. Our fusion model is proven to resist severe noise
interference like Gaussian, motion blur, and frost, with only slight
degradation. The experiment results demonstrate the benefits of our adaptive
fusion. Our analysis on the robustness of multi-modal fusion will provide
further insights for future research.
