To solve the problem of low target detection accuracy caused by closely arranged targetscomplex background information and numerous small targets in remote sensing imagesa remote sensing image target detection algorithm based on the improved ConvNeXt is proposed by using YOLOv5s.Firstlyan improved ConvNeXt Block is introduced at the bottom of the feature extraction network to widen the perceptual field and enrich the semantic information through the interaction between large kernel convolution and selfattention.Secondlya set of bottomup pyramidal structures is added to the part of multiscale feature fusion to amplify the role of shallow feature maps and compensate for the position information of small targets in remote sensing imageswhich is lost due to deep convolution.Finallythe SIoU loss function is introduced to redefine the penalty index and accelerate the convergence of the overall network.The proposed detection algorithm is ablated on the RSOD dataset with a mean Average Precision（mAP） of 92.27%and the experimental results show that the proposed algorithm can realize accurate detection of remote sensing image targets.