In order to improve the accuracy of semantic segmentation in outdoor scenes,an improved DeepLabV3+ neural network segmentation algorithm is proposed.The backbone part adopts a grouped ResNest network,thus the training weights of various types of targets account for different percentages.Then,the Atrous Spatial Pyramid Pooling (ASPP) module is improved by using densely connected mode to expand the perceptual field without sacrificing feature spatial resolution and to improve feature reuse efficiency.The decoding side fuses the low-level semantic features extracted from the encoding side at three different scales to recover the spatial information lost in the downsampling process.The experimental results show that the algorithm not only improves the segmentation accuracy of the target in the detection of CityScape dataset,but also has a significant improvement in both full-scene understanding and detail processing ability.