The existing semantic segmentation algorithms have too many parameters and huge memory usage, so that it is difficult to meet the requirements real-world applications such as automatic driving. In order to solve the problem, a novel, effective and lightweight real-time semantic segmentation algorithm based on Separable Pyramid Module (SPM) was proposed. Firstly, factorized convolution and dilated convolution were adopted in the form of a feature pyramid to construct the bottleneck structure, providing a simple but effective way to extract local and contextual information. Then, the Context Channel Attention (CCA) module based on computer vision attention was proposed to modify the channel weights of shallow feature maps by utilizing deep semantic features, thereby optimizing the segmentation results. Experimental results show that without pre-training or any additional processing, the proposed algorithm achieves mean Intersection-over-Union (mIoU) of 71.86% on Cityscapes test set at the speed of 91 Frames Per Second (FPS). Compared to Efficient Residual Factorized ConvNet (ERFNet), the proposed algorithm has the mIoU 3.86 percentage points higher, and the processing speed of 2.2 times. Compared with the latest Light-weighted Network with Efficient Reduced Non-local operation for real-time semantic segmentation (LRNNet), the proposed algorithm has the mIoU slightly lower by 0.34 percentage points, but the processing speed increased by 20 FPS. The experimental results show that the proposed algorithm has great value for completing tasks such as efficient and accurate street scene image segmentation required in automatic driving.