The increasing demands for high-precision semantic segmentation in applications such as autonomous driving, unmanned aerial vehicles, and robotics has made improving segmentation accuracy a major research focus. In this paper, we propose MPNet, (multi-scale progressive network) a novel semantic segmentation model based on the DeepLabV3+ architecture. First, a lightweight MobileNetV2 was employed as the backbone network, and a new multi-scale feature fusion structure was constructed by integrating the backbone with the centralized feature pyramid network (CFPNet). Then, based on the ASPP module, a progressive dilated atrous spatial pyramid pooling (PDASPP) module was designed to further enhance feature extraction. Extensive experiments were conducted on the Cityscapes and PASCAL VOC 2012 Augmented. The experimental results show that MPNet achieved 75.23% mIoU and 83.54% mPA on the Cityscapes dataset, outperforming DeepLabV3+ by 3.01% and 3.31%, respectively. On the PASCAL VOC 2012 Augmented dataset, MPNet achieved 72.70% mIoU and 83.57% mPA, with improvements of 1.59% and 1.30% over DeepLabV3+, respectively. These results demonstrate that MPNet significantly improves segmentation accuracy while maintaining model complexity under control, providing an effective solution for semantic segmentation in road scene understanding.