It is difficult to detect and identify dim and small targets on the sea surface in complex backgrounds, such as low contrast, shore island background, and foreground occlusion. This study proposes a method based on infrared polarization images to detect dim and small targets on the sea surface. Herein, we propose an improved hue-saturation-intensity (HIS) color space fusion algorithm based on the perceived color of the human visual system, which combines infrared polarization information and infrared intensity information of sea surface image. We design a sea surface region segmentation method based on the infrared polarization image, and the sea surface region is segmented as a candidate target enhancement region. The context-aware saliency algorithm is used to calculate the saliency of the sea surface HSI color space fusion image. The sea surface HSI color space fusion image is corrected using the saliency map to obtain a dim and small target enhanced image. The contrast of target and background and the local signal-to-noise ratio are used to evaluate the features of the fusion enhanced image. Results obtained show that in comparison with the existing methods, the proposed method can enhance dim and small targets and suppress the background interference. The evaluation index of the proposed method is higher than that of other existing methods, which provides support for the detection and identification of surface ship targets.