Polarization is one of the important characteristics of light. Polarization imaging technology can obtain the intensity information and polarization information of the target in the scene. Polarization information can reflect the material characteristics of the target surface. In this paper, two image enhancement schemes based on polarization information are proposed to meet the accuracy requirements of common target recognition results in road scenes under haze weather conditions. First of all, the polarization data set is constructed through multiple acquisition experiments, data cleaning and image labeling, with a total of 4 649 images and 31 877 tags. For the scene with slight haze pollution, the sky region in the polarization intensity image is segmented by the region automatic growth algorithm, and the reflected light of the target is reversely generated according to the polarization degree and polarization angle information of the sky region and the atmospheric physical scattering model, so as to realize the image defogging. For the heavily polluted scene of haze, wavelet transform is used to enhance the image, and the degree of polarization image is used to enhance the target contour in the intensity image. The image gray variance and image information entropy are used as image quality evaluation indicators, and the YOLO v5s deep learning network is used for object detection. The results show that in the case of light haze pollution, the image quality and object detection accuracy have been improved, the image information entropy has increased by 3. 36%, the gray variance has increased by 40. 27%, and the object detection mAP has reached 76. 40%, increased by 12. 69%. In the case of heavy smog pollution, the object detection mAP increased by about 1. 69%.