Semantic segmentation is widely used in automatic driving systems. To quickly and accurately classify objects in emergency situations, a large number of images need to be processed per second. To make a semantic segmentation model run on hardware with low memory and limited computing capacity, this paper proposes a real-time semantic segmentation network called MRFDCNet. This architecture is based on our proposed multireceptive field dense connection (MRFDC) module. The module uses one depthwise separable convolution branch and two depthwise dilated separable convolution branches with a proposed symmetric sequence of dilation rates to obtain local and contextual information under multiple receptive fields. In addition, we utilize a dense connection to allow local and contextual information to complement each other. We design a guided attention (GA) module to effectively utilize deep and shallow features. The GA module uses high-level semantic context to guide low-level spatial details and fuse both types of feature representations. MRFDCNet has only 1.07 M parameters, and it can achieve 72.8% mIoU on the Cityscapes test set with 74 FPS on one NVIDIA GeForce GTX 1080 Ti GPU. Experiments on the Cityscapes and CamVid test sets show that MRFDCNet achieves a balance between accuracy and inference speed. Code is available at https://github.com/Wsky1836/MRFDCNet.

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