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. 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 the 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 are 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.

很少有研究专门针对多雨环境中的实时语义分割提出。然而，该领域的需求巨大，并且对于轻量级网络而言是挑战。因此，本文提出了一种专为雨天环境中的前景分割而设计的轻量级网络，称为De-raining语义分割网络（DRSNet）。通过分析雨滴的特性，针对性地设计了MultiScaleSE块以对输入图像进行编码，它使用多尺度膨胀卷积来增加接收场，并使用SE注意机制来学习每个通道的权重。为了结合不同编码器和解码器层之间的语义信息，建议使用非对称跳过，即，编码器的较高语义层采用双线性插值，输出经过逐点卷积，然后逐元素添加到解码器的较低语义层。根据控制实验，在前景准确度指标上，MultiScaleSE块和不对称跳过的性能分别与SEResNet18和对称跳过相比有所提高。DRSNet的参数和运算浮点（FLOP）分别仅为0.54M和0.20GFLOP。在UESTC的“全天候增雨（UAS-add-rain）”和“百度人群细分增雨（BPS-add-rain）”基准上，都获得了最新的结果和实时性能。输入尺寸为192 * 128、384 * 256和768 * 512。DRSNet的速度超过了1GFLOP内的所有网络，