To address the degradation problem of traditional depth estimation models caused by image quality degradation in haze environment， a model based on Conditional Generative Adversarial Network （CGAN） was proposed to estimate the depth of single haze image by fusing dual attention mechanism. Firstly， for the network structure of the generator of the model， the DenseUnet structure fused with dual attention mechanism was proposed. The dense blocks were used as basic blocks in the encoding and decoding processes of U-net. Dense and jump connections were used to enhance information flow， as well as extract the underlying structural features and high-level depth information of the direct transmission rate map. Then， the global dependencies of spatial features and channel features were adaptively adjusted by the dual attention module. At the same time， a new structure-preserving loss function was proposed by combining the least absolute value function， perceptual loss， gradient loss， and adversarial loss. Finally， using the direct transmission rate map of the haze image as a condition of CGAN， the depth map of the haze image was estimated through the adversarial learning of the generator and the discriminator. Training and testing were performed on the indoor dataset NYU Depth v2 and the outdoor dataset DIODE. Experimental results show that the proposed model has a finer geometric structure and richer local details. Compared with the fully convolutional residual network， on NYU Depth v2， the proposed model has the Logarithmic Mean Error （LME） and Root Mean Square Error （RMSE） error reduced by 7% and 10%， respectively. Compared with the deep ordinal regression network， on DIODE， the proposed model has the accuracy with threshold less than 1.25 increased by 7.6%. It can be seen that the proposed model improves the estimation accuracy and generalization ability of depth estimation under the interference of haze.