In order to solve the problem of ship intelligent perception system based on the vision, under the scattering effect of sea fog suspended particles, the visibility and contrast of image information has significantly reduced, the highlevel visual tasks such as target detection, target tracking and semantic segmentation were seriously affected, resulting in the inability to detect targets or the loss of tracking targets, the Maritime Haze data set was created, and by combining with physical models and depth learning methods, a cycle generation adversarial network image dehazing model based on prior knowledge of dark channel was proposed. A dark channel prior dehazing algorithm was used to decompose an image into transmission map and dehazing image. Then, the generator and discriminator of a cycle generative adversarial network were used to process and judge the dark channel prior output to generate a better dehazing image. The experiments show that the evaluation indexes of peak signal to noise ratio (PSNR) and structural similarity (SSMI) of the model reaches 24.73 and 0.943 respectively, which are superior to 23.34 (PSNR) and 0.921 (SSMI) of PSD and 19.19 (PSNR) and 0.584 (SSMI) of CycleGAN with excellent clarity and color authenticity in visual effects, and leading other methods in marine image dehazing.