Semantic segmentation finds wide-ranging applications and stands as a crucial task in the realm of computer vision. It holds significant implications for scene comprehension and decision-making in unmanned systems, including domains such as autonomous driving, unmanned aerial vehicles, robotics, and healthcare. Consequently, there is a growing demand for high precision in semantic segmentation, particularly for these contents. This paper introduces DPNet, a novel image semantic segmentation method based on the Deeplabv3 plus architecture. (1) DPNet utilizes ResNet-50 as the backbone network to extract feature maps at various scales. (2) Our proposed method employs the BiFPN (Bi-directional Feature Pyramid Network) structure to fuse multi-scale information, in conjunction with the ASPP (Atrous Spatial Pyramid Pooling) module, to handle information at different scales, forming a dual pyramid structure that fully leverages the effective features obtained from the backbone network. (3) The Shuffle Attention module is employed in our approach to suppress the propagation of irrelevant information and enhance the representation of relevant features. Experimental evaluations on the Cityscapes dataset and the PASCAL VOC 2012 dataset demonstrate that our method outperforms current approaches, showcasing superior semantic segmentation accuracy.