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Optimizing remote sensing target detection is of great significance to military and people's livelihood. Due to the blurred image, small target and large number of detected objects in the remote sensing data, the detection accuracy is not high. A new network is proposed: the new network uses the Mish activation function to replace the SiLU (Sigmaid Weighted Liner Unit) activation function on the basis of the original YOLOv5s (You Only Look Once v5s) network; In order to solve the problem of small targets in remote sensing images, SPD-Conv (Space-to-depth-Conv) module which is friendly to small targets and low resolution is adopted; Considering the conflict between regression and classification tasks when using coupling detector heads, the decoupled detector heads in YOLOX (You Only Look Once X) are used to improve the model precision. The experimental results show that compared with the original YOLOv5s, the new network has improved the average accuracy of mAP (mean average precision) by 7%, the recall rate by 10.9% and the detection speed by 16.95%. The improved network model has obvious advantages over the original model, and the recognition effect is significantly improved.