Polarization imaging target detection is of great significance for man made target detection.Pixel coupling is a polarization imaging method using polarization intensity data in four directions as a super pixel.The polarization parameters of super pixels reduce the resolution of the image to a quarter of the original image,which is not conducive to the detection of small targets.The polarization parameter analyses of the pixel coupled image generate noise,which can interfere with the detection of small targets.In this paper,a target detection algorithm based on YOLOv5s network with the addition of a polarization information analysis module(Covcat)is proposed.The algorithm achieves end to end pixel coupled polarization imaging target detection,using network for polarization analysis,multi convolution information fusion to improve feature extraction ability,and average detection accuracy(mAP)of targets.The algorithm is verified by using the aerial drone data set,showing that the average detection accuracy of the algorithm is improved by 4 percentage points and 5 percentage points and 12 percentage points compared with the intensity,polarization and polarization angles maps obtained using polarization parameters.