Due to the process defects of the micro-polarizer array and the matching error between the polarizer array and the focal plane array, the polarization modulation of the incident light with the same intensity and the same polarization direction is different, resulting in more blind pixels and more serious non-uniformity problems in the polarization integrated imaging system. Due to the influence of blind pixels and non-uniformity, infrared images will introduce noise, information loss and error increase. Therefore, the data preprocessing of polarization integrated detection system is particularly important. In this paper, the linear response model of the polarization detector is established. On the basis of the response correction, the parameters of the micro-polarizer are calibrated, and the polarization correction of the incident radiation is realized. A blind pixel detection method based on nonlinear least squares fitting Marius curve is proposed. The standard curves of each channel are calibrated. Using the curve deviation and fitting parameters, the simultaneous detection of response blind pixel and polarization blind pixel is achieved. The self-developed mid-wave infrared polarization integrated detector is used for experimental verification. The correction results show that the non-uniformity after polarization correction is reduced by 98.14% and 40.46% respectively compared with the original data and response correction data. 563 response blind pixels and 86 polarization blind pixels are detected. The imaging results show that the blind pixel recognition of this method is accurate, while the national standard method has the problem of blind pixel missed detection. The corrected polarization information can highlight the target contour of the imaging scene. The problem of the original data of the detector is effectively solved, which provides a reference for the data processing and polarization imaging quality improvement of the polarization integrated detector. This paper first analyzes the classification and causes of blind pixels. Due to the limitations of traditional detection methods and the influence of focal plane inhomogeneity, it is impossible to accurately determine the response blind pixels and polarization blind pixels. On the basis of non-uniformity correction, a nonlinear least squares fitting Marius response curve is proposed, and blind pixel detection is realized by means of curve deviation and fitting parameters. The experimental verification was carried out based on the polarization integrated detector developed by our research group. There are 563 response blind pixels detected by curve deviation and 86 polarization blind pixels detected by fitting parameters. The results show that the method in this paper can detect blind pixels more accurately than the national standard method, and reduce missed detection to a certain extent. Finally, the blind pixel compensation and imaging work are carried out. The imaging results show that the polarization imaging can better highlight the edge contour contrast of the target. The data preprocessing method of polarization integrated infrared detector in this paper can provide reference for subsequent high-quality polarization imaging detection.