Seeking to resolve the issue of missed and incorrectdetection of smalltargets in remote sensing images, this study proposes an optimized YOLOv7-tiny algorithm. Firstly, a multi-scale attention efficient multi-scale attention (EMA) module is introduced, and based on this, the ELAN-EMA, a multi-scale feature extraction module, is incorporated to to greatly enhance the backbone network's proficiency in extracting features across various scales. Secondly, the feature pyramid network (FPN) is introduced with the content-aware reassembly offeatures (CARAFE) optimization, which expandsthereceptivefieldand enablestheacquisition ofmoredetailed information and rich semantic information of small targets. Finally, this study adopts the normalized wasserstein distance (NWD) loss function to optimize the complete intersection over union (CIoU) loss function, and designs the NWD-CIoU loss function, which reduces the sensitivity ofCIoU to smalltargetposition shifts and can betterimprove the detection performanceofsmall targets. Experiments conducted on the publicly available remote sensing datasetsRSOD and NWPU VHR-10 show that compared withthebaselinemodel, theoptimized modelachievesa3.6% and 1.8% increasein mAP@0.5, respectively, with slightlyincreased computational and parameter requirements, markedly enhancing the accuracy with which small targets are detected in remote sensing images. The comprehensive performance meets the requirements for deployment in remote sensing detection systems.