Crowd density estimation is an important part of intelligent crowd monitoring. However, there are still many problems in density estimation due to the complexity of crowd scenes. Aiming at the high-density scenes with varied scales, we present a method based on cross-adversarial loss and global feature for crowd counting, so as to achieve the purpose of capturing more feature details and reducing the impact of background noise more effectively. First, we use the cross-adversarial loss to generate the residual map, which makes use of the consistency between different scales and solves the homogenization problem of fused density map. Then, we extract large-range context information and focus on key information in global spatial features for the generation of a residual map. Finally, the high-resolution density map is used to estimate the crowd counting. Experiments on three datasets confirm that the proposed method has good adaptability in scenes with obvious distribution change, not just in extracting high-quality features for density map estimation but also for accurate crowd counting.