  While the deep learning-based image deraining methods have made great
progress in recent years, there are two major shortcomings in their application
in real-world situations. Firstly, the gap between the low-level vision task
represented by rain removal and the high-level vision task represented by
object detection is significant, and the low-level vision task can hardly
contribute to the high-level vision task. Secondly, the quality of the
deraining dataset needs to be improved. In fact, the rain lines in many
baselines have a large gap with the real rain lines, and the resolution of the
deraining dataset images is generally not ideally. Meanwhile, there are few
common datasets for both the low-level vision task and the high-level vision
task. In this paper, we explore the combination of the low-level vision task
with the high-level vision task. Specifically, we propose an end-to-end object
detection network for reducing the impact of rainfall, which consists of two
cascaded networks, an improved image deraining network and an object detection
network, respectively. We also design the components of the loss function to
accommodate the characteristics of the different sub-networks. We then propose
a dataset based on the KITTI dataset for rainfall removal and object detection,
on which our network surpasses the state-of-the-art with a significant
improvement in metrics. Besides, our proposed network is measured on driving
videos collected by self-driving vehicles and shows positive results for rain
removal and object detection.
