Regarding the problem that lightweight algorithm in remote sensing aircraft target detection is difficult to balance accuracy and real-time performancea model compression method based on YOLOv4 structured pruning is presented.In order to make the anchor frame parameters more suitable for remote sensing datasets and take advantage of network multi-scale detectionK-means++ algorithm is used to cluster the datasets and scale adaptive adjustment is designed to restrain the redundancy of the anchor frame caused by too many small targets and close target sizes.In additionin order to reduce the parameters of the modelthe scaling factor γ in the normalization layer is used for L1 sparse regularizationthe filter and convolution kernel weights are re-evaluatedchannels with less feature information are iteratively prunedand then the pruning model is fine-tuned to recover accuracy.The experimental results show that after pruningthe model parameters are compressed by 93.1%and the detection speed is 2.46 times faster than that of the original modelwhich can effectively improve the detection accuracy and real-time performance.