Abstract: Abstract： Aiming at the problems that existing remote sensing image target detection methods suffer from the loss of local feature information in deep CNNs and low detection accuracy of complex scenes， a target detection method based on lightweight YOLOv4 is proposed. Firstly， the lightweight neural network Ghostnet is used to replace the cspdarknet53 network used as the backbone feature extraction in YOLOv4. Secondly， to improve the complex environment detection capability， CycleGAN is used to simulate night scenes， and again， the transformer module is fused to make the model easy to capture inter-feature relationships and local information of the network. Finally， Adam optimiser and K-means++ screening anchor frame are used to accelerate the convergence speed， and the example is validated with RSOD aerial remote sensing dataset. The experimental results show that the MAP value is improved by 6.65 percentage points and the number of parameters is reduced by 84.7% compared with the original YOLOv4， i.e. the algorithm in this paper can meet the real-time target detection of aircraft on the airport field in complex scenes.

Key words: Key words： real-time target detection, remote sensing image, complicated scene, airport field