Fog (haze) can significantly reduce the imaging quality of visible light detection equipment based on image-guided weapons, thus interfering with the accurate recognition of targets. To solve this problem, a single image defogging algorithm based on conditional generation countermeasure network is proposed. Soft pooling operation is used in the sampling of the generator to improve the extraction ability of fine-grained features. The global average pooling layer is added to eliminate the oscillation effect of image edges and improve the definition of defogged images. The structure of the discriminator is simplified, and the method for determining the weight value of the loss function is optimized to improve the training efficiency of the network model. The experimental results show that the defogged image is clear and sharp with natural color, and it is superior to the classical defogging algorithm in objective quantitative indicators such as structure similarity, peak signal to noise ratio and image information entropy. The average accuracy of target detection in defogged image is improved by 4.13%.