With the technological upgrades of modern warfare, there is a growing need for faster, farther and more accurate target detection in the field of airborne infrared detection. In this paper, an improved target detection algorithm based on YOLOv7 is proposed to meet the high-precision and high-frame-rate detection of infrared dim dim targets in airborne environment. Firstly, based on the YOLOv7 target detection algorithm, the network structure is modified and the number of convolutional layers is deepened to extract more features of small target information. Moreover, the attention mechanism is introduced into the feature layer obtained by the backbone network to improve the perception ability of the neural network to perceive the small targets and increase the weight share of the region where the small targets are located. Finally, the EIOU loss function is used to replace the CIOU loss function, which improves the convergence speed and positioning accuracy. The experimental results show that compared with the original algorithm YOLOv7, the improved algorithm can reach 98.49% mAP with minimal loss of frame rate, which is 1.24% higher than the original algorithm, and it helps to improve the detection accuracy of airborne infrared dim small targets.