In response to the limitations of existing deep learning-based object detection methods when faced with real-world unmanned aerial vehicle (UAV) targets, such as poor robustness, low accuracy, and high model complexity, a YOLO-based object detection method called OD-YOLO is proposed. This algorithm addresses the characteristics of UAV targets being small, slow, and low. Several improvements have been implemented. Firstly, to tackle the issue of learning information loss and insufficient emphasis on target information during the downsampling process, a spatial-to-depth convolution is introduced to ensure the preservation of learning information while highlighting the features of UAV targets. Secondly, to further enhance the accuracy of object detection and improve its generalization across different backgrounds, a full-dimensional dynamic convolution is be used. This enhances the accuracy of object detection and improves its generalization capabilities across various backgrounds. Lastly, the backbone network of the model is modified to enhance the semantic features of UAV targets and reduce the size of the skeleton, resulting in a reduced parameter count and improved computational efficiency of the model, while maintaining effective representation capabilities for UAV targets. Experimental simulations were conducted to compare OD-YOLO with current state-of-the-art object detection algorithms. The results demonstrate significant improvements in accuracy and lightweight performance for OD-YOLO. The mAP and Recall distributions increased by 3.4% and 5.1%, respectively, compared to the original model.