Based on polarization imaging characteristics and deep feature classification requirements, an object detection method based on deep sparse feature learning of salient polarization parameters is proposed. First, the salient polarization parameter image is constructed as the source image based on polarization analysis. Then the sparse feature of the image to be detected is learned by discriminant dictionary pair, and the object is classified and located by the dictionary pair which is used as the classifier in CNN framework. Finally, the typical object and scene are selected for data acquisition and model training according to the practical application requirements of polarization imaging detection, and some simulation experiments are conducted. The results show that the detecting score and average detection precision of the proposed method are improved at different degrees by comparing to the polarization direction detection methods and the effectiveness of this method is verified. The proposed method has application value for improving the detection ability of polarization imaging effectively.