Aiming at the problem of low target detection accuracy caused by numerous small targets in remote sensing images, drastic changes in target scales and complex backgrounds, a target detection algorithm based on improved YOLOX is proposed. On the basis of YOLOX, firstly, an attention mechanism is added to the backbone network to improve the network′s ability to perceive small targets in remote sensing images and enrich the semantic information; secondly, the feature fusion part is added to the MSCER multiscale information fusion module in the feature fusion part to reduce the loss of image detail information caused by scale changes in remote sensing images through feature maps of different sizes; finally, the convergence speed of the network is accelerated by introducing CIoU loss function to make it meet the demand of realtime. In this paper, the proposed detection algorithm is experimented on the RSOD remote sensing dataset, and the average detection accuracy is 9512%, which is 869% higher than that of the unimproved YOLOX. The experimental results prove that the proposed method has higher detection accuracy.