This study proposes a semantic segmentation network called LDPANet to address the challenges of significant variations in target sizes and the difficulty of efficient extraction of multi-scale features in semantic segmentation tasks of street scene images. Firstly, the void convolution is combined with the deeply separable convolution introduced into the residual learning unit to optimize the encoder structure, which reduces computational complexity and alleviates the problem of gradient vanishing. Secondly, the network utilizes a layer-wise iterative void spatial pyramid to sequentially fuse top-down feature information, enhancing the effective interaction of contextual information. After multi-scale feature fusion, an attribute attention module is introduced to suppress redundant information and strengthen important features. Furthermore, channel-extended upsampling replaces two-wire interpolation upsampling as the decoder to further improve the resolution of feature maps. Finally, the accuracy of the LDPANet method on Cityscapes and CamVid datasets reaches 91.8% and 87.52%, respectively. Compared with the network model in recent years, the proposed network model can accurately extract pixel position information and spatial dimension information and improve the accuracy of semantic segmentation.