Many applications rely on the accuracy of data understanding, and semantic image segmentation effectively solves this problem, which provides the necessary context information for scene understanding based on pixel level.A U-Net network structure, ECAU-Net, is proposed based on ResNeXt50.Compared with the general convolution operation, ResNeXt50 has a stronger feature extraction ability.In fusion process, Efficient Channel Attention module(ECA-Net) is introduced to further enhance the ability of feature representation to discriminate scene segmentation.In addition, the introduction of dilated convolution in the overall network structure expands the receptive field of the image without changing the size of convolution kernel, thereby maximizing the performance of the network.The experimental results show that on the CamVid dataset, compared with U-Net, ECAU-Net has improved the Acc, Acc class, MIoU and FWIoU respectively by 2.1%, 8.6%, 8.2% and 3.2%.