Aiming at the problems of accuracy and efficiency of military personnel image segmentation in multiple complex environments, we propose an encoder-decoder network based on improved dense atrous convolution and serial attention modules. First, we add the dense atrous convolution module in the U-shaped encoder-decoder network to improve the network's ability to segment multiscale targets and reduce parameter amounts. Second, we add the serial attention module in the U-shaped encoder-decoder network, enabling the network to focus more on the important features in the image. Finally, convolution after each downsampling in the encoder structure of the U-shaped encoder-decoder network is improved to reduce the parameter amounts. The experimental results on the multiple environments camouflage dataset show that the mean intersection of the union of the proposed network is 2.27 percent, 4.93 percent, and 10.46 percent higher than U-Net, SegNet, and FCN-8s, respectively. The parameter amounts are significantly reduced, improving the effectiveness of the network for military personnel segmentation in multiple complex environments.