To address the phenomena of incorrect segmentation and missing segmentation that occur when the current Deeplab v3+ model does not adequately employ high-resolution shallow features,an improved Deeplab v3+ feature image semantic segmentation algorithm that incorporates multi-scale features is proposed.In the backbone network,multi-scale pyramidal convolution is introduced.The standard convolution in the pooled pyramid of atrous space convolution is replaced by the deep separable convolution to reduce the number of parameters of the whole model.Finally,a multi-scale approach is adopted in the decoding layer to capture the global background,and the background features are combined with the shallow features and the atrous space pyramid pooling layer through the attention mechanism to enrich the semantic information of the fused shallow features.Experiments show that in CityScapes dataset,the proposed algorithm has a better edge segmentation effect,with an Mean Intersection over Union(MIoU) of 74.76%, which is 2.20% higher than that of the original algorithm.Compared with advanced algorithms,it is also proved that it is effective in improving incorrect segmentation and missing segmentation.