In view of the large number of small targets with wide scale scope in aerial images,the YOLOv5s structure is improved from four aspects,and the LL-YOLOv5s network is proposed.The improvement measures mainly include deleting the feature layer of 32 times undersampling in the backbone network and using only two detection heads,so that the model can focus on the detection of small targets.The LL-YOLOv5s improves the mAP by 2.9 percentage points on the DOTA-v1.5 aerial dataset.Then,a simple and efficient self-attention module called qv self-attention module is proposed,which is added to the position before the first detection head of LL-YOLOv5s,and the mAP is further improved by 0.9 percentage points at the cost of adding a small amount of calculation.It is found that the combination of qv self-attention module with convolution layer further improves the mAP by 0.4 percentage points.Compared with YOLOv5s,the improved model greatly reduces the number of parameters and improves the detection accuracy significantly at the cost of adding a small amount of calculation.