In response to the problem of the large parameter size of semantic segmentation networks, making it difficult to deploy on memory-constrained edge devices, a lightweight real-time semantic segmentation algorithm is proposed based on BiLevelNet. Firstly, dilated convolutions are employed to augment the receptive field, and feature reuse strategies are integrated to enhance the network's region awareness. Next, a two-stage PBRA (Partial Bi-Level Route Attention) mechanism is incorporated to establish dependencies between distant objects, thereby augmenting the network's global perception capability. Finally, the FADE operator is introduced to combine shallow features to improve the effectiveness of image upsampling. Experimental results show that, at an input image resolution of 512×1024, the proposed network achieves an average Intersection over Union (IoU) of 75.1% on the Cityscapes dataset at a speed of 121 frames per second, with a model size of only 0.7 M. Additionally, at an input image resolution of 360×480, the network achieves an average IoU of 68.2% on the CamVid dataset. Compared with other real-time semantic segmentation methods, this network achieves a balance between speed and accuracy, meeting the real-time requirements for applications like autonomous driving.