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Unmanned Aerial Vehicles (UAVs) are indispensable in promoting the development of remote sensing technology. Nevertheless, the tasks of object recognition in remote sensing images based on UAV platforms face major difficulties and challenges due to the complex and variable background environments and the high-density distribution of objects. This paper proposes an object detection algorithm for UAV remote sensing images—YOLO-Remote, which aims to improve detection accuracy by enhancing YOLOv8. This algorithm innovatively integrates the SaElayer module to enhance the focus on remote sensing targets and improve network efficiency. Additionally, it introduces the Efficient-SPPF structure, which effectively expands the network’s receptive field and promotes deep learning capabilities. To address sample imbalance and improve bounding box localization and classification performance, the study also designs the Focaler-MDPIOU strategy. With these comprehensive optimizations, YOLO-Remote achieves significant progress in network architecture. Experiments were conducted on the NWPU VHR10 and RSOD datasets, and the experimental results show that compared to the base model YOLOv8n, the improved model’s average precision increased by 2.7% and 3.2% respectively, demonstrating its superiority in the field of object detection for UAV remote sensing images.The code is available at https://github.com/QuincyQAQ/Yolo-Remotehttps://github.com/QuincyQAQ/Yolo-Remote.