To solve the problems of complex and diverse background,dense targets and big scale differences in remote sensing images,which are prone to cause missed detection and false detection of small targets,a target detection algorithm of remote sensing images based on context information is proposed by taking YOLOv5s algorithm as the basic framework of the network.Firstly,Context Module (CM) is designed and added to the backbone network to enlarge the perception range of the target area features,obtain more context information,and improve the ability of the model for small-scale target detection.Secondly,Coordinate Attention (CA) module is introduced into the feature backbone network to strengthen the models ability to recognize the target position information in the shallow network.Finally,the Spatial Pyramid Pooling (SPP) module is replaced with the Atrous Spatial Pyramid Pooling (ASPP) module to realize the fusion of global information and local information,and further enhance the semantic information of small targets.The experimental results show that the mAP50 of the improved algorithm is 97.9% on the RSOD dataset, which is 1.7 percentage point higher than that of the original YOLOv5s algorithm.The FPS reaches 71 frames per second,which meets the requirements of real-time detection.Compared with other detection algorithms,the improved algorithm has a lower missed detection rate and false detection rate,and the detection performance is better.