When airborne infrared detection system detects targets in the near-earth background, the ground will disturb the weak and small targets seriously, which leads to detection performance reduction of traditional detection methods for weak and small targets. In view of this problem, the generative adversarial networks are utilized to propose a weak and small target detection method of airborne infrared detection system in the near-earth background. The generative adversarial networks are constructed based on deep auto-encoder, and the inception mechanism is introduced to extract multiple scaled features of vision information, the residual block is also introduced to mitigate the vanishing gradient issue. In the adversarial training of the neural networks, the loss function of the generator consists of the moving loss and the adversarial loss, which improves the training effect of the generator. In the end, the experiments are carried on the public unmanned drone airborne infrared detection datasets, and the results show that the proposed method can detect the infrared weak and small targets in the near-earth background successfully, at the same time, the average precision and speed of the detection method are better than the compared methods.