A lightweight dual-input change detection network, D-WNet, is proposed to address the issues of traditional semantic segmentation networks being susceptible to interference from shadows and other ground objects, as well as the rough boundary segmentation of buildings. The new network starts with W-Net and uses deep separable convolutional blocks and hollow space pyramid pooling modules to replace the originally cumbersome convolutional and downsampling processes. It utilizes a right-line feature encoder to enhance the fusion of high-dimensional and high-dimensional features and introduces channels and spatiotemporal attention mechanisms in the sampling section of the decoder to obtain effective features of the network in different dimensions. The resulting D-WNet has significantly improved performance. Experiments were conducted on the publicly available WHU and LEVIR-CD building change detection datasets, and the results were compared with the W-Net, U-Net, ResNet, SENet, and DeepLabv3+ semantic segmentation networks. The experimental results show that D-WNet performs well in five indicators (intersection-to-intersection ratio, F1 value, recall rate, accuracy rate, and running time) and has more accurate change detection results for shadow interference and building edge areas.