Seeking to resolve the issue of missed and incorrect detection of small targets in remote sensing images, this study proposes an optimized YOLOv7-tiny algorithm. Firstly, a multi-scale attention Efficient Multi-Scale Attention Mod-ule (EMA) 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 of Features (CARAFE) optimi-zation, which expands the receptive field and enables the acquisition of more detailed 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 of CIoU to small target position shifts and can better improve the detection performance of small targets. Experiments conducted on the publicly available remote sensing datasets RSOD and NWPU VHR-10 show that compared with the baseline model, the optimized model achieves a 3.6% and 1.8% increase in mAP50, re-spectively, with slightly increased 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.