Objective Aiming at the problem of poor detection accuracy of existing remote sensing image object detection algorithms in scenes with large variations of object?? s scales and complex background information interference a remote sensing image object detection algorithm MSD-YOLO Multi-Scale Downsampling-YOLO based on multi-scale downsampling was proposed. Methods Firstly a multi-scale downsampling MSD module was designed which employed three parallel downsampling branches to extract multi-scale features simultaneously during downsampling. This approach avoided the problem of severe loss of small target feature information after many times of downsampling in existing models. Additionally an ACON Activate or Not activation function with adaptive activation characteristics was introduced to enhance the model?? s generalization ability. Secondly the Triplet Attention mechanism was improved and the Improved Triplet Attention ITA mechanism was proposed to adaptively adjust the weight allocation of feature maps by capturing cross-dimensional interactions and emphasizing spatial attention. This mechanism improved the detection performance of the model in scenes with complex background information interference. Results The experimental results showed that the mean average precisions mAP of MSD-YOLO on NWPU VHR-10 and RSOD datasets reached 94. 9% and 96. 8% respectively which were both improved by 1. 5% compared with the baseline network YOLOv7 and the precisions outperformed those of other classical network models. Conclusion The proposed MSD-YOLO algorithm effectively improves detection accuracy in scenarios with significant scale variations and complex background interference and has valuable applications in remote sensing image object detection scenarios.