In order to effectively detect weak and small infrared targets under complex background, a single-frame method based on horizontal-vertical multi-scale grayscale difference （HV-MSGD） is proposed to enhance weak targets, and the strong edges of background are suppressed by the difference between the distance and grayscale values. There is discontinuity between the target area and the surrounding area. To strengthen their differences, HV-MSGD combined with bilateral filtering （BF） can increase the intensity of the target while suppressing the background. Candidate targets are further extracted by adaptive local threshold segmentation and global threshold segmentation. In order to further verify the impact on single-frame detection, the above-mentioned single-frame detection algorithm is combined with an improved untraced Kalman particle filter （UPF） to implement trajectory detection. The experimental results show that this method is better than other methods under weak signal-to-noise ratio （SNR）. It can enhance the target while suppressing the background, and the enhancement effect is 6-30 times that of other methods. In the experiments, the input signal-to-noise ratios were 2.78, 1.77, 1.79, 1.13, and 1.16, respectively. After image processing, the background suppression factors （BSFs） are 13.48, 21.33, 11.73, 20.63, and 121.92, and the signal-to-noise ratio gains （GSNRs） are 40.09, 71.37, 27.53, 12.65, and 131, respectively. The probability of detection （Pd） of this method is also superior to other algorithms. When the false alarm rates （FARs） are 5×10-4, 1×10-3, 1×10-3, 1×10-5, and 7×10-6, the Pd values of the five sets using real sequence images are calculated to be 94.4%, 92.2%, 91.3%, 95.6% and 96.7% respectively.