To improve the detection ability of infrared small targets in complex background, an enhanced algorithm based on SSD and DSST is proposed. The channel space attention mechanism and FPN algorithm are added to the SSD basic network to enhance the semantic information of the deep network, optimize the target receptive field, and use convolution to calculate the target feature information to enhance the detection ability of small targets. Depending on DSST method of scale discrimination, the problem of target loss is solved, and the stable detection of continuous frames is realized. A simulation system which comprises target fusion, target trajectory setting, target recognition and tracking is built. It is combining PC with embedded ZYNQ platform for the detection of small targets under complex background. The preset scenes using for efficient simulation and testing are constructed based on the superposition and fusion of different targets under complex background, which avoids complex field verification experiments. The efficiency for the algorithm is evaluated in simulation system. The experimental validating test shows that the proposed algorithm could effectively identify small targets when detecting background areas with other moving targets and interference noise. The AP value of multi frame detection in different background areas is 98.17%, which is 11.28% higher than the traditional SSD algorithm, reflecting the effectiveness of the algorithm.