In the field of target detection, the Single Shot multibox Detector(SSD) target detection network based on deep learning has two advantages of good real-time performance and high accuracy. Because the infrared image of special vehicles was difficult to obtain, the infrared image of car and bus were taken as the research object, the Pascal VOC dataset of infrared image was constructed, the SSD network was trained, and the infrared target image was detected by the trained network. The results show that the more the feature information of the infrared target, the higher the detection accuracy, but the problem of “missing detection” of the vehicle with missing information exists in the infrared image. In response to this problem, the data structure was optimized by adding the "incomplete window module", and the problem of "missing detection" of the vehicle was effectively solved, and the detection accuracy of the target as a whole was also significantly improved. The infrared target detection result after improving the data set was used as the evaluation index, which can accurately evaluate the infrared stealth camouflage effect of special vehicles under complex background.