Aiming at the problem that the current semantic segmentation algorithms are difficult to reach the balance between real-time reasoning and high-precision segmentation， a Squeezing and Refining Network （SRNet） was proposed to improve real-time performance of reasoning and accuracy of segmentation. Firstly， One-Dimensional （1D） dilated convolution and bottleneck-like structure unit were introduced into Squeezing and Refining （SR） unit， which greatly reduced the amount of calculation and the number of parameters of model. Secondly， the multi-scale Spatial Attention （SA） confusing module was introduced to make use of the spatial information of shallow layer features efficiently. Finally， the encoder was formed through stacking SR units， and two SA units were used to form the decoder. Simulation shows that SRNet obtains 68.3% Mean Intersection over Union （MIoU） on Cityscapes dataset with only 30 MB parameters and 8.8×10⁹ FLoating-point Operation Per Second （FLOPS）. Besides， the model reaches a forward reasoning speed of 12.6 Frames Per Second （FPS） with input pixel size of 512×1 024×3 on a single NVIDIA Titan RTX card. Experimental results imply that the designed lightweight model SRNet reaches a good balance between accurate segmentation and real-time reasoning， and is suitable for scenarios with limited computing power and power consumption.