In lightweight networks, the speed of semantic segmentation is high but the accuracy is low. On the basis of lightweight networks, a real-time semantic segmentation method based on dilated convolution smoothing and lightweight up-sampling is proposed. To improve segmentation speed, a lightweight network, ResNeXt-18, with structured knowledge distillation is used as feature extraction network. To improve the segmentation accuracy, a dilated convolution smoothing module and a lightweight up-sampling module are designed. To verify the effectiveness of the proposed method, the evaluations are carried out using the Cityscapes and CamVid datasets, obtaining the speed of 40.2 frame/s and the segmentation accuracy of 76.8%, with a parameter count of 1.18×10 7. The experimental results demonstrate that the proposed method can obtain high segmentation accuracy while maintaining its high-speed real-time performance; as such, it has certain practical value.