  Few researches have been proposed specifically for real-time semantic
segmentation in rainy environments. However, the demand in this area is huge
and it is challenging for lightweight networks. Therefore, this paper proposes
a lightweight network which is specially designed for the foreground
segmentation in rainy environments, named De-raining Semantic Segmentation
Network (DRSNet). By analyzing the characteristics of raindrops, the
MultiScaleSE Block is targetedly designed to encode the input image, it uses
multi-scale dilated convolutions to increase the receptive field, and SE
attention mechanism to learn the weights of each channels. In order to combine
semantic information between different encoder and decoder layers, it is
proposed to use Asymmetric Skip, that is, the higher semantic layer of encoder
employs bilinear interpolation and the output passes through pointwise
convolution, then added element-wise to the lower semantic layer of decoder.
According to the control experiments, the performances of MultiScaleSE Block
and Asymmetric Skip compared with SEResNet18 and Symmetric Skip respectively
are improved to a certain degree on the Foreground Accuracy index. The
parameters and the floating point of operations (FLOPs) of DRSNet is only 0.54M
and 0.20GFLOPs separately. The state-of-the-art results and real-time
performances are achieved on both the UESTC all-day Scenery add rain
(UAS-add-rain) and the Baidu People Segmentation add rain (BPS-add-rain)
benchmarks with the input sizes of 192*128, 384*256 and 768*512. The speed of
DRSNet exceeds all the networks within 1GFLOPs, and Foreground Accuracy index
is also the best among the similar magnitude networks on both benchmarks.
