Abstract: In order to solve the problems of complex network calculation and low detection accuracy of remote sensing aircraft detection algorithm, this paper improves the research from two aspects of improving accuracy and simplifying model based on the mainstream network YOLOv4, and proposes a lightweight multi-scale monitoring network LDS_YOLO（light dense supervision YOLO）. Aiming at the problem of insufficient extraction of remote sensing aircraft target details, the structure of three groups of multi-scale fusion prediction layers is improved, and the dense connection mode is designed between the four convolution blocks before the first up-sampling of each branch, which can enhance the fusion of aircraft of different scales, enrich the feature details and improve the prediction accuracy. Aiming at the problem of low correlation degree of target features, the consistency supervision loss function is introduced to improve the detection accuracy while assisting prediction by supervision classification network. By adding lightweight modules containing global average pooling layer, full connection layer and feature mapping layer, the channel structure is adjusted to reduce feature redundancy of the weight model and reduce the number of network parameters. On the basis of ensuring the detection rate, the number of model parameters is reduced to 3.6×106, and the computational cost is 77?MFLOPs. The test detection rate loss is less than 2.3% compared with the original model, and the speed is up to 17 frame/s. The anti-over-fitting ability and robustness of the lightweight algorithm model are analyzed by comparing with the mainstream detection algorithm, and the validity and feasibility of the lightweight remote sensing aircraft target detection algorithm are proved.